{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\kgatter1\AppData\Local\SURFdrive\data\R&P data final\Spitzenkandidaten results_final.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Feb 2020, 13:58:51

{com}. do "C:\Users\kgatter1\AppData\Local\Temp\STD12c414_000000.tmp"
{txt}
{com}. 
. xtset ID
{txt}{col 8}panel variable:  {res}ID (balanced)
{txt}
{com}. 
. regress CandidateRecognition w5_newsexposure_mean knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration PTV SeveralDomParties i.candidatename OwnCandidate female age education 

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}    94,948
{txt}{hline 13}{c +}{hline 34}   F(18, 94929)    = {res}  1201.00
{txt}       Model {c |} {res} 2713.63361        18  150.757423   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res}  11916.141    94,929  .125526878   {txt}R-squared       ={res}    0.1855
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.1853
{txt}       Total {c |} {res} 14629.7746    94,947   .15408359   {txt}Root MSE        =   {res}  .3543

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}CandidateRecognition{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      t{col 54}   P>|t|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
w5_newsexposure_mean {c |}{col 22}{res}{space 2} .0190828{col 34}{space 2} .0013806{col 45}{space 1}   13.82{col 54}{space 3}0.000{col 62}{space 4} .0163767{col 75}{space 3} .0217888
{txt}{space 4}knowledge_stable {c |}{col 22}{res}{space 2} .0233314{col 34}{space 2} .0009294{col 45}{space 1}   25.10{col 54}{space 3}0.000{col 62}{space 4} .0215098{col 75}{space 3} .0251531
{txt}{space 9}intefficacy {c |}{col 22}{res}{space 2} .0090567{col 34}{space 2}  .001021{col 45}{space 1}    8.87{col 54}{space 3}0.000{col 62}{space 4} .0070556{col 75}{space 3} .0110579
{txt}{space 3}w5_polinterest_eu {c |}{col 22}{res}{space 2}  .029569{col 34}{space 2} .0008528{col 45}{space 1}   34.67{col 54}{space 3}0.000{col 62}{space 4} .0278975{col 75}{space 3} .0312405
{txt}{space 1}turnoutintention_w5 {c |}{col 22}{res}{space 2} .0012911{col 34}{space 2} .0006464{col 45}{space 1}    2.00{col 54}{space 3}0.046{col 62}{space 4}  .000024{col 75}{space 3} .0025581
{txt}{space 7}euintegration {c |}{col 22}{res}{space 2} .0048965{col 34}{space 2} .0007276{col 45}{space 1}    6.73{col 54}{space 3}0.000{col 62}{space 4} .0034705{col 75}{space 3} .0063226
{txt}{space 17}PTV {c |}{col 22}{res}{space 2}  .002387{col 34}{space 2} .0003727{col 45}{space 1}    6.40{col 54}{space 3}0.000{col 62}{space 4} .0016564{col 75}{space 3} .0031175
{txt}{space 3}SeveralDomParties {c |}{col 22}{res}{space 2} .0503334{col 34}{space 2} .0026292{col 45}{space 1}   19.14{col 54}{space 3}0.000{col 62}{space 4} .0451802{col 75}{space 3} .0554866
{txt}{space 20} {c |}
{space 7}candidatename {c |}
{space 9}Timmermans  {c |}{col 22}{res}{space 2} .0931641{col 34}{space 2} .0038971{col 45}{space 1}   23.91{col 54}{space 3}0.000{col 62}{space 4} .0855258{col 75}{space 3} .1008024
{txt}{space 8}Verhofstadt  {c |}{col 22}{res}{space 2}-.0414195{col 34}{space 2} .0042493{col 45}{space 1}   -9.75{col 54}{space 3}0.000{col 62}{space 4} -.049748{col 75}{space 3}-.0330909
{txt}{space 11}Vestager  {c |}{col 22}{res}{space 2}-.0437938{col 34}{space 2} .0042382{col 45}{space 1}  -10.33{col 54}{space 3}0.000{col 62}{space 4}-.0521007{col 75}{space 3}-.0354869
{txt}{space 11}Eickhout  {c |}{col 22}{res}{space 2}-.1864986{col 34}{space 2} .0042829{col 45}{space 1}  -43.54{col 54}{space 3}0.000{col 62}{space 4}-.1948931{col 75}{space 3} -.178104
{txt}{space 13}Keller  {c |}{col 22}{res}{space 2}-.1784317{col 34}{space 2} .0042829{col 45}{space 1}  -41.66{col 54}{space 3}0.000{col 62}{space 4}-.1868261{col 75}{space 3}-.1700372
{txt}{space 11}Zahradil  {c |}{col 22}{res}{space 2}-.1090975{col 34}{space 2} .0043752{col 45}{space 1}  -24.94{col 54}{space 3}0.000{col 62}{space 4}-.1176729{col 75}{space 3}-.1005222
{txt}{space 20} {c |}
{space 8}OwnCandidate {c |}{col 22}{res}{space 2} .3477791{col 34}{space 2} .0038904{col 45}{space 1}   89.39{col 54}{space 3}0.000{col 62}{space 4}  .340154{col 75}{space 3} .3554043
{txt}{space 14}female {c |}{col 22}{res}{space 2}-.0158227{col 34}{space 2} .0023772{col 45}{space 1}   -6.66{col 54}{space 3}0.000{col 62}{space 4} -.020482{col 75}{space 3}-.0111634
{txt}{space 17}age {c |}{col 22}{res}{space 2} .0000212{col 34}{space 2} .0000777{col 45}{space 1}    0.27{col 54}{space 3}0.785{col 62}{space 4} -.000131{col 75}{space 3} .0001734
{txt}{space 11}education {c |}{col 22}{res}{space 2} .0073222{col 34}{space 2} .0006805{col 45}{space 1}   10.76{col 54}{space 3}0.000{col 62}{space 4} .0059884{col 75}{space 3}  .008656
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-.1845983{col 34}{space 2}  .008044{col 45}{space 1}  -22.95{col 54}{space 3}0.000{col 62}{space 4}-.2003645{col 75}{space 3} -.168832
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. estat vif 

{txt}    Variable {c |}       VIF       1/VIF  
{hline 13}{c +}{hline 22}
w5_newsexp~n {c |} {res}     1.13    0.883697
{txt}knowledge_~e {c |} {res}     1.30    0.766389
{txt}{space 1}intefficacy {c |} {res}     1.57    0.635210
{txt}w5_polinte~u {c |} {res}     1.64    0.608308
{txt}turnoutint~5 {c |} {res}     1.26    0.794125
{txt}euintegrat~n {c |} {res}     1.16    0.862344
{txt}{space 9}PTV {c |} {res}     1.05    0.950452
{txt}SeveralDom~s {c |} {res}     1.27    0.787158
{txt}candidaten~e {c |}
{space 10}2  {c |} {res}     1.67    0.599472
{txt}{space 10}3  {c |} {res}     1.55    0.644771
{txt}{space 10}4  {c |} {res}     1.54    0.648139
{txt}{space 10}5  {c |} {res}     1.58    0.634673
{txt}{space 10}6  {c |} {res}     1.58    0.634681
{txt}{space 10}7  {c |} {res}     1.58    0.631652
{txt}OwnCandidate {c |} {res}     1.05    0.948750
{txt}{space 6}female {c |} {res}     1.07    0.935786
{txt}{space 9}age {c |} {res}     1.10    0.909204
{txt}{space 3}education {c |} {res}     1.15    0.870267
{txt}{hline 13}{c +}{hline 22}
    Mean VIF {c |} {res}     1.35
{txt}
{com}. 
. *** ====== ANALYSES IN MANUSCRIPT ===================
. *** Figure 1 (descriptives only; Figure done in Excel) 
. tab CandidateRecognition country if Eickhout==1, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 Candidate {c |}
recognitio {c |}                                                    country
   n Weber {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         0 {c |}{res}     1,176      1,315      1,789      1,095      1,459        467      1,387      1,560      1,263      1,140 {txt}{c |}{res}    12,651 
           {txt}{c |}{res}     78.04      80.63      90.17      62.61      70.04      36.23      84.73      69.24      79.99      86.69 {txt}{c |}{res}     74.30 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       331        316        195        654        624        822        250        693        316        175 {txt}{c |}{res}     4,376 
           {txt}{c |}{res}     21.96      19.37       9.83      37.39      29.96      63.77      15.27      30.76      20.01      13.31 {txt}{c |}{res}     25.70 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      1,631      1,984      1,749      2,083      1,289      1,637      2,253      1,579      1,315 {txt}{c |}{res}    17,027 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab CandidateRecognition country if Keller==1, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 Candidate {c |}
recognitio {c |}                                                    country
   n Weber {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         0 {c |}{res}     1,176      1,315      1,789      1,095      1,459        467      1,387      1,560      1,263      1,140 {txt}{c |}{res}    12,651 
           {txt}{c |}{res}     78.04      80.63      90.17      62.61      70.04      36.23      84.73      69.24      79.99      86.69 {txt}{c |}{res}     74.30 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       331        316        195        654        624        822        250        693        316        175 {txt}{c |}{res}     4,376 
           {txt}{c |}{res}     21.96      19.37       9.83      37.39      29.96      63.77      15.27      30.76      20.01      13.31 {txt}{c |}{res}     25.70 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      1,631      1,984      1,749      2,083      1,289      1,637      2,253      1,579      1,315 {txt}{c |}{res}    17,027 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab CandidateRecognition country if Timmermans==1, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 Candidate {c |}
recognitio {c |}                                                    country
   n Weber {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         0 {c |}{res}     1,176      1,315      1,789      1,095      1,459        467      1,387      1,560      1,263      1,140 {txt}{c |}{res}    12,651 
           {txt}{c |}{res}     78.04      80.63      90.17      62.61      70.04      36.23      84.73      69.24      79.99      86.69 {txt}{c |}{res}     74.30 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       331        316        195        654        624        822        250        693        316        175 {txt}{c |}{res}     4,376 
           {txt}{c |}{res}     21.96      19.37       9.83      37.39      29.96      63.77      15.27      30.76      20.01      13.31 {txt}{c |}{res}     25.70 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      1,631      1,984      1,749      2,083      1,289      1,637      2,253      1,579      1,315 {txt}{c |}{res}    17,027 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab CandidateRecognition country if Verhofstadt==1, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 Candidate {c |}
recognitio {c |}                                                    country
   n Weber {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         0 {c |}{res}     1,176      1,315      1,789      1,095      1,459        467      1,387      1,560      1,263      1,140 {txt}{c |}{res}    12,651 
           {txt}{c |}{res}     78.04      80.63      90.17      62.61      70.04      36.23      84.73      69.24      79.99      86.69 {txt}{c |}{res}     74.30 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       331        316        195        654        624        822        250        693        316        175 {txt}{c |}{res}     4,376 
           {txt}{c |}{res}     21.96      19.37       9.83      37.39      29.96      63.77      15.27      30.76      20.01      13.31 {txt}{c |}{res}     25.70 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      1,631      1,984      1,749      2,083      1,289      1,637      2,253      1,579      1,315 {txt}{c |}{res}    17,027 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab CandidateRecognition country if Vestager==1, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 Candidate {c |}
recognitio {c |}                                                    country
   n Weber {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         0 {c |}{res}     1,176      1,315      1,789      1,095      1,459        467      1,387      1,560      1,263      1,140 {txt}{c |}{res}    12,651 
           {txt}{c |}{res}     78.04      80.63      90.17      62.61      70.04      36.23      84.73      69.24      79.99      86.69 {txt}{c |}{res}     74.30 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       331        316        195        654        624        822        250        693        316        175 {txt}{c |}{res}     4,376 
           {txt}{c |}{res}     21.96      19.37       9.83      37.39      29.96      63.77      15.27      30.76      20.01      13.31 {txt}{c |}{res}     25.70 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      1,631      1,984      1,749      2,083      1,289      1,637      2,253      1,579      1,315 {txt}{c |}{res}    17,027 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab CandidateRecognition country if Weber==1, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 Candidate {c |}
recognitio {c |}                                                    country
   n Weber {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         0 {c |}{res}     1,176      1,315      1,789      1,095      1,459        467      1,387      1,560      1,263      1,140 {txt}{c |}{res}    12,651 
           {txt}{c |}{res}     78.04      80.63      90.17      62.61      70.04      36.23      84.73      69.24      79.99      86.69 {txt}{c |}{res}     74.30 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       331        316        195        654        624        822        250        693        316        175 {txt}{c |}{res}     4,376 
           {txt}{c |}{res}     21.96      19.37       9.83      37.39      29.96      63.77      15.27      30.76      20.01      13.31 {txt}{c |}{res}     25.70 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      1,631      1,984      1,749      2,083      1,289      1,637      2,253      1,579      1,315 {txt}{c |}{res}    17,027 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. tab CandidateRecognition country if Zahradil==1, col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

 Candidate {c |}
recognitio {c |}                                                    country
   n Weber {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         0 {c |}{res}     1,176      1,315      1,789      1,095      1,459        467      1,387      1,560      1,263      1,140 {txt}{c |}{res}    12,651 
           {txt}{c |}{res}     78.04      80.63      90.17      62.61      70.04      36.23      84.73      69.24      79.99      86.69 {txt}{c |}{res}     74.30 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       331        316        195        654        624        822        250        693        316        175 {txt}{c |}{res}     4,376 
           {txt}{c |}{res}     21.96      19.37       9.83      37.39      29.96      63.77      15.27      30.76      20.01      13.31 {txt}{c |}{res}     25.70 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      1,631      1,984      1,749      2,083      1,289      1,637      2,253      1,579      1,315 {txt}{c |}{res}    17,027 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. 
. 
. *** Table 1 - Model 1
. xtmelogit CandidateRecognition w5_newsexposure_mean knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration female age education PTV SeveralDomParties i.candidatename OwnCandidate || country: , variance
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-35000.339}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-34884.358}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-34845.073}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-34845.073}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-34830.303}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-34830.281}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-34830.281}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}    94,948
{txt}Group variable: {res}country{col 49}{txt}Number of groups{col 67}={col 70}{res}       10

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}     6,249
{txt}{col 63}avg{col 67}={col 69}{res}   9,494.8
{txt}{col 63}max{col 67}={col 69}{res}    11,904

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}18{txt}){col 67}={col 70}{res} 12504.45
{txt}Log likelihood = {res}-34830.281{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}CandidateRecognition{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
w5_newsexposure_mean {c |}{col 22}{res}{space 2} .1984584{col 34}{space 2}  .012147{col 45}{space 1}   16.34{col 54}{space 3}0.000{col 62}{space 4} .1746507{col 75}{space 3} .2222661
{txt}{space 4}knowledge_stable {c |}{col 22}{res}{space 2} .1718973{col 34}{space 2} .0084011{col 45}{space 1}   20.46{col 54}{space 3}0.000{col 62}{space 4} .1554313{col 75}{space 3} .1883632
{txt}{space 9}intefficacy {c |}{col 22}{res}{space 2} .1229746{col 34}{space 2} .0090505{col 45}{space 1}   13.59{col 54}{space 3}0.000{col 62}{space 4} .1052358{col 75}{space 3} .1407133
{txt}{space 3}w5_polinterest_eu {c |}{col 22}{res}{space 2} .2669998{col 34}{space 2}  .007698{col 45}{space 1}   34.68{col 54}{space 3}0.000{col 62}{space 4}  .251912{col 75}{space 3} .2820876
{txt}{space 1}turnoutintention_w5 {c |}{col 22}{res}{space 2} .0214731{col 34}{space 2} .0058945{col 45}{space 1}    3.64{col 54}{space 3}0.000{col 62}{space 4} .0099201{col 75}{space 3} .0330262
{txt}{space 7}euintegration {c |}{col 22}{res}{space 2} .0497594{col 34}{space 2} .0060028{col 45}{space 1}    8.29{col 54}{space 3}0.000{col 62}{space 4} .0379942{col 75}{space 3} .0615246
{txt}{space 14}female {c |}{col 22}{res}{space 2}-.1278249{col 34}{space 2} .0198325{col 45}{space 1}   -6.45{col 54}{space 3}0.000{col 62}{space 4}-.1666958{col 75}{space 3} -.088954
{txt}{space 17}age {c |}{col 22}{res}{space 2} .0029549{col 34}{space 2} .0006693{col 45}{space 1}    4.41{col 54}{space 3}0.000{col 62}{space 4} .0016431{col 75}{space 3} .0042667
{txt}{space 11}education {c |}{col 22}{res}{space 2} .0460991{col 34}{space 2} .0056977{col 45}{space 1}    8.09{col 54}{space 3}0.000{col 62}{space 4} .0349318{col 75}{space 3} .0572664
{txt}{space 17}PTV {c |}{col 22}{res}{space 2} .0227457{col 34}{space 2}  .003059{col 45}{space 1}    7.44{col 54}{space 3}0.000{col 62}{space 4} .0167502{col 75}{space 3} .0287412
{txt}{space 3}SeveralDomParties {c |}{col 22}{res}{space 2} .2567894{col 34}{space 2} .0286815{col 45}{space 1}    8.95{col 54}{space 3}0.000{col 62}{space 4} .2005747{col 75}{space 3} .3130042
{txt}{space 20} {c |}
{space 7}candidatename {c |}
{space 9}Timmermans  {c |}{col 22}{res}{space 2} .5251321{col 34}{space 2} .0277243{col 45}{space 1}   18.94{col 54}{space 3}0.000{col 62}{space 4} .4707935{col 75}{space 3} .5794706
{txt}{space 8}Verhofstadt  {c |}{col 22}{res}{space 2}-.0292765{col 34}{space 2} .0341589{col 45}{space 1}   -0.86{col 54}{space 3}0.391{col 62}{space 4}-.0962266{col 75}{space 3} .0376736
{txt}{space 11}Vestager  {c |}{col 22}{res}{space 2}-.2058186{col 34}{space 2} .0343586{col 45}{space 1}   -5.99{col 54}{space 3}0.000{col 62}{space 4}-.2731602{col 75}{space 3}-.1384771
{txt}{space 11}Eickhout  {c |}{col 22}{res}{space 2}-2.094072{col 34}{space 2}  .050663{col 45}{space 1}  -41.33{col 54}{space 3}0.000{col 62}{space 4} -2.19337{col 75}{space 3}-1.994774
{txt}{space 13}Keller  {c |}{col 22}{res}{space 2}-1.649573{col 34}{space 2} .0453705{col 45}{space 1}  -36.36{col 54}{space 3}0.000{col 62}{space 4}-1.738497{col 75}{space 3}-1.560648
{txt}{space 11}Zahradil  {c |}{col 22}{res}{space 2}  -1.1286{col 34}{space 2} .0379214{col 45}{space 1}  -29.76{col 54}{space 3}0.000{col 62}{space 4}-1.202925{col 75}{space 3}-1.054276
{txt}{space 20} {c |}
{space 8}OwnCandidate {c |}{col 22}{res}{space 2}  2.51818{col 34}{space 2} .0331674{col 45}{space 1}   75.92{col 54}{space 3}0.000{col 62}{space 4} 2.453173{col 75}{space 3} 2.583187
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-5.411124{col 34}{space 2} .2093514{col 45}{space 1}  -25.85{col 54}{space 3}0.000{col 62}{space 4}-5.821445{col 75}{space 3}-5.000802
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}country{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .3775669{col 44} .1693655{col 58} .1567363{col 70} .9095324
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}{help j_chibar##|_new:chibar2(01) =} {res}3611.49{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. 
. *** Table 1 - Model 2
. xtmelogit CandidateRecognition w5_newsexposure_mean knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration female age education SeveralDomParties i.candidatename OwnCandidate || country: , variance
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-44379.866}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res: -44250.59}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: -44241.26}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -44241.26}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-44232.791}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-44232.763}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-44232.763}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}   119,189
{txt}Group variable: {res}country{col 49}{txt}Number of groups{col 67}={col 70}{res}       10

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}     9,023
{txt}{col 63}avg{col 67}={col 69}{res}  11,918.9
{txt}{col 63}max{col 67}={col 69}{res}    15,771

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}17{txt}){col 67}={col 70}{res} 14948.74
{txt}Log likelihood = {res}-44232.763{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}CandidateRecognition{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
w5_newsexposure_mean {c |}{col 22}{res}{space 2} .2104015{col 34}{space 2} .0105383{col 45}{space 1}   19.97{col 54}{space 3}0.000{col 62}{space 4} .1897469{col 75}{space 3}  .231056
{txt}{space 4}knowledge_stable {c |}{col 22}{res}{space 2} .1550927{col 34}{space 2}  .007382{col 45}{space 1}   21.01{col 54}{space 3}0.000{col 62}{space 4} .1406242{col 75}{space 3} .1695612
{txt}{space 9}intefficacy {c |}{col 22}{res}{space 2} .1175413{col 34}{space 2} .0080227{col 45}{space 1}   14.65{col 54}{space 3}0.000{col 62}{space 4} .1018171{col 75}{space 3} .1332654
{txt}{space 3}w5_polinterest_eu {c |}{col 22}{res}{space 2} .2727563{col 34}{space 2} .0068278{col 45}{space 1}   39.95{col 54}{space 3}0.000{col 62}{space 4}  .259374{col 75}{space 3} .2861386
{txt}{space 1}turnoutintention_w5 {c |}{col 22}{res}{space 2} .0107274{col 34}{space 2} .0052475{col 45}{space 1}    2.04{col 54}{space 3}0.041{col 62}{space 4} .0004425{col 75}{space 3} .0210123
{txt}{space 7}euintegration {c |}{col 22}{res}{space 2} .0473468{col 34}{space 2} .0052388{col 45}{space 1}    9.04{col 54}{space 3}0.000{col 62}{space 4} .0370789{col 75}{space 3} .0576147
{txt}{space 14}female {c |}{col 22}{res}{space 2}-.0887449{col 34}{space 2} .0176012{col 45}{space 1}   -5.04{col 54}{space 3}0.000{col 62}{space 4}-.1232425{col 75}{space 3}-.0542472
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.0002693{col 34}{space 2} .0006035{col 45}{space 1}   -0.45{col 54}{space 3}0.655{col 62}{space 4} -.001452{col 75}{space 3} .0009135
{txt}{space 11}education {c |}{col 22}{res}{space 2} .0371511{col 34}{space 2} .0050932{col 45}{space 1}    7.29{col 54}{space 3}0.000{col 62}{space 4} .0271685{col 75}{space 3} .0471337
{txt}{space 3}SeveralDomParties {c |}{col 22}{res}{space 2} .3486005{col 34}{space 2} .0206705{col 45}{space 1}   16.86{col 54}{space 3}0.000{col 62}{space 4}  .308087{col 75}{space 3}  .389114
{txt}{space 20} {c |}
{space 7}candidatename {c |}
{space 9}Timmermans  {c |}{col 22}{res}{space 2} .5267075{col 34}{space 2} .0270507{col 45}{space 1}   19.47{col 54}{space 3}0.000{col 62}{space 4} .4736891{col 75}{space 3} .5797259
{txt}{space 8}Verhofstadt  {c |}{col 22}{res}{space 2}-.0982724{col 34}{space 2} .0286388{col 45}{space 1}   -3.43{col 54}{space 3}0.001{col 62}{space 4}-.1544034{col 75}{space 3}-.0421413
{txt}{space 11}Vestager  {c |}{col 22}{res}{space 2}-.3087909{col 34}{space 2} .0289986{col 45}{space 1}  -10.65{col 54}{space 3}0.000{col 62}{space 4} -.365627{col 75}{space 3}-.2519547
{txt}{space 11}Eickhout  {c |}{col 22}{res}{space 2}-1.770826{col 34}{space 2} .0401044{col 45}{space 1}  -44.16{col 54}{space 3}0.000{col 62}{space 4}-1.849429{col 75}{space 3}-1.692223
{txt}{space 13}Keller  {c |}{col 22}{res}{space 2}-1.355263{col 34}{space 2} .0362371{col 45}{space 1}  -37.40{col 54}{space 3}0.000{col 62}{space 4}-1.426287{col 75}{space 3} -1.28424
{txt}{space 11}Zahradil  {c |}{col 22}{res}{space 2}-1.106476{col 34}{space 2} .0337631{col 45}{space 1}  -32.77{col 54}{space 3}0.000{col 62}{space 4} -1.17265{col 75}{space 3}-1.040301
{txt}{space 20} {c |}
{space 8}OwnCandidate {c |}{col 22}{res}{space 2}    2.419{col 34}{space 2} .0312174{col 45}{space 1}   77.49{col 54}{space 3}0.000{col 62}{space 4} 2.357815{col 75}{space 3} 2.480185
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} -5.09408{col 34}{space 2} .1956292{col 45}{space 1}  -26.04{col 54}{space 3}0.000{col 62}{space 4}-5.477506{col 75}{space 3}-4.710654
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}country{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .3369992{col 44} .1511202{col 58} .1399347{col 70} .8115817
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}{help j_chibar##|_new:chibar2(01) =} {res}4204.24{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. 
. *** Table 1 - Model 3
. xtmelogit CandidateRecognition w5_newsexposure_mean knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration female age education SeveralDomParties i.candidatename OwnCandidate || country: w5_newsexposure_mean, variance
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-44348.778}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-44226.038}  (not concave)
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-44214.817}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-44214.817}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-44212.524}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-44201.268}  (not concave)
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-44195.603}  (not concave)
{res}{txt}Iteration 4:{space 3}log likelihood = {res:-44193.232}  
{res}{txt}Iteration 5:{space 3}log likelihood = {res:-44191.822}  
{res}{txt}Iteration 6:{space 3}log likelihood = {res:-44191.788}  
{res}{txt}Iteration 7:{space 3}log likelihood = {res:-44191.788}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}   119,189
{txt}Group variable: {res}country{col 49}{txt}Number of groups{col 67}={col 70}{res}       10

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}     9,023
{txt}{col 63}avg{col 67}={col 69}{res}  11,918.9
{txt}{col 63}max{col 67}={col 69}{res}    15,771

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}17{txt}){col 67}={col 70}{res} 14136.56
{txt}Log likelihood = {res}-44191.788{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}CandidateRecognition{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
w5_newsexposure_mean {c |}{col 22}{res}{space 2} .2243668{col 34}{space 2} .0403103{col 45}{space 1}    5.57{col 54}{space 3}0.000{col 62}{space 4} .1453601{col 75}{space 3} .3033735
{txt}{space 4}knowledge_stable {c |}{col 22}{res}{space 2} .1570908{col 34}{space 2} .0073937{col 45}{space 1}   21.25{col 54}{space 3}0.000{col 62}{space 4} .1425994{col 75}{space 3} .1715822
{txt}{space 9}intefficacy {c |}{col 22}{res}{space 2} .1172213{col 34}{space 2} .0080329{col 45}{space 1}   14.59{col 54}{space 3}0.000{col 62}{space 4} .1014771{col 75}{space 3} .1329654
{txt}{space 3}w5_polinterest_eu {c |}{col 22}{res}{space 2} .2724486{col 34}{space 2} .0068407{col 45}{space 1}   39.83{col 54}{space 3}0.000{col 62}{space 4} .2590411{col 75}{space 3} .2858562
{txt}{space 1}turnoutintention_w5 {c |}{col 22}{res}{space 2}  .010464{col 34}{space 2} .0052562{col 45}{space 1}    1.99{col 54}{space 3}0.047{col 62}{space 4}  .000162{col 75}{space 3}  .020766
{txt}{space 7}euintegration {c |}{col 22}{res}{space 2} .0467348{col 34}{space 2} .0052536{col 45}{space 1}    8.90{col 54}{space 3}0.000{col 62}{space 4} .0364379{col 75}{space 3} .0570317
{txt}{space 14}female {c |}{col 22}{res}{space 2}-.0886799{col 34}{space 2}  .017627{col 45}{space 1}   -5.03{col 54}{space 3}0.000{col 62}{space 4}-.1232283{col 75}{space 3}-.0541316
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.0000782{col 34}{space 2} .0006045{col 45}{space 1}   -0.13{col 54}{space 3}0.897{col 62}{space 4} -.001263{col 75}{space 3} .0011067
{txt}{space 11}education {c |}{col 22}{res}{space 2} .0376351{col 34}{space 2} .0050995{col 45}{space 1}    7.38{col 54}{space 3}0.000{col 62}{space 4} .0276402{col 75}{space 3} .0476299
{txt}{space 3}SeveralDomParties {c |}{col 22}{res}{space 2} .3468332{col 34}{space 2} .0206775{col 45}{space 1}   16.77{col 54}{space 3}0.000{col 62}{space 4}  .306306{col 75}{space 3} .3873603
{txt}{space 20} {c |}
{space 7}candidatename {c |}
{space 9}Timmermans  {c |}{col 22}{res}{space 2} .5268978{col 34}{space 2} .0270939{col 45}{space 1}   19.45{col 54}{space 3}0.000{col 62}{space 4} .4737947{col 75}{space 3}  .580001
{txt}{space 8}Verhofstadt  {c |}{col 22}{res}{space 2} -.098147{col 34}{space 2} .0286645{col 45}{space 1}   -3.42{col 54}{space 3}0.001{col 62}{space 4}-.1543284{col 75}{space 3}-.0419655
{txt}{space 11}Vestager  {c |}{col 22}{res}{space 2} -.310118{col 34}{space 2} .0290313{col 45}{space 1}  -10.68{col 54}{space 3}0.000{col 62}{space 4}-.3670183{col 75}{space 3}-.2532178
{txt}{space 11}Eickhout  {c |}{col 22}{res}{space 2}-1.769979{col 34}{space 2} .0400925{col 45}{space 1}  -44.15{col 54}{space 3}0.000{col 62}{space 4}-1.848558{col 75}{space 3}-1.691399
{txt}{space 13}Keller  {c |}{col 22}{res}{space 2}-1.354955{col 34}{space 2} .0362471{col 45}{space 1}  -37.38{col 54}{space 3}0.000{col 62}{space 4}-1.425998{col 75}{space 3}-1.283912
{txt}{space 11}Zahradil  {c |}{col 22}{res}{space 2} -1.10822{col 34}{space 2} .0337645{col 45}{space 1}  -32.82{col 54}{space 3}0.000{col 62}{space 4}-1.174397{col 75}{space 3}-1.042043
{txt}{space 20} {c |}
{space 8}OwnCandidate {c |}{col 22}{res}{space 2} 2.409731{col 34}{space 2} .0311971{col 45}{space 1}   77.24{col 54}{space 3}0.000{col 62}{space 4} 2.348586{col 75}{space 3} 2.470876
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-5.169166{col 34}{space 2}  .312184{col 45}{space 1}  -16.56{col 54}{space 3}0.000{col 62}{space 4}-5.781035{col 75}{space 3}-4.557297
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}country{txt}: Independent{col 30}{c |}
{col 16}var(w5_new~n){col 30}{c |}{res}{col 33} .0148538{col 44} .0073152{col 58} .0056576{col 70} .0389977
{txt}{col 19}var(_cons){col 30}{c |}{res}{col 33} .9238239{col 44} .4246384{col 58} .3752597{col 70} 2.274294
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}chi2({res}2{txt}) = {res}4286.19{col 59}{txt}Prob > chi2 ={col 73}{res}0.0000

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. 
. 
. *** Figure 2
. predict u*, reffects
{txt}
{com}. bysort country: generate groups=(_n==1) 
{txt}
{com}. list country u2 u1 if groups
{txt}
        {c TLC}{hline 13}{c -}{hline 11}{c -}{hline 11}{c TRC}
        {c |} {res}    country          u2          u1 {txt}{c |}
        {c LT}{hline 13}{c -}{hline 11}{c -}{hline 11}{c RT}
     1. {c |} {res}    Czechia    .7881274    -.102553 {txt}{c |}
 10550. {c |} {res}    Denmark     .871768   -.1530746 {txt}{c |}
 29296. {c |} {res}     France   -1.595007    .2267896 {txt}{c |}
 43184. {c |} {res}    Germany   -.7795219   -.0367859 {txt}{c |}
 63449. {c |} {res}     Greece    .1791666    .0037884 {txt}{c |}
        {c LT}{hline 13}{c -}{hline 11}{c -}{hline 11}{c RT}
 78030. {c |} {res}    Hungary    .7779865   -.0162759 {txt}{c |}
 97252. {c |} {res}Netherlands    .6656972   -.0973706 {txt}{c |}
118434. {c |} {res}     Poland     1.15118   -.0824188 {txt}{c |}
134205. {c |} {res}      Spain   -.9623623     .149757 {txt}{c |}
154274. {c |} {res}     Sweden   -1.084735    .1059802 {txt}{c |}
        {c BLC}{hline 13}{c -}{hline 11}{c -}{hline 11}{c BRC}

{com}. gen intercept = _b[_cons] + u2
{txt}
{com}. gen slope = _b[w5_newsexposure_mean] + u1
{txt}
{com}. gen yhat= intercept + (slope*w5_newsexposure_mean)
{txt}(44,289 missing values generated)

{com}. 
. twoway connected yhat w5_newsexposure_mean, connect(L) by(country)
{res}{txt}
{com}. 
. 
. *** ====== ANALYSES IN APPENDIX ===========
. *** Appendix B 
. *** Table A4
. 
. tab PTV country if Weber==1

           {txt}{c |}                                                    country
       PTV {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       508        871        650        750        775      1,370        608        566      1,297        365 {txt}{c |}{res}     7,760 
{txt}       1.5 {c |}{res}        51          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        51 
{txt}         2 {c |}{res}       129        262        156        208        151        100        165        150        120         90 {txt}{c |}{res}     1,531 
{txt}       2.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         3 {c |}{res}        77        274        147        201        136         92        153        153        153        105 {txt}{c |}{res}     1,491 
{txt}       3.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         4 {c |}{res}        85        250        153        204        145         95        207        169        123        116 {txt}{c |}{res}     1,547 
{txt}       4.5 {c |}{res}        74          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        74 
{txt}         5 {c |}{res}        78        214        141        148         94         72        161        145        106         70 {txt}{c |}{res}     1,229 
{txt}       5.5 {c |}{res}        58          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        58 
{txt}         6 {c |}{res}       107        240        270        378        202        248        242        276        265        118 {txt}{c |}{res}     2,346 
{txt}       6.5 {c |}{res}        38          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        38 
{txt}         7 {c |}{res}        28        141        134        190        100         86        163        153        157         75 {txt}{c |}{res}     1,227 
{txt}       7.5 {c |}{res}        21          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        21 
{txt}         8 {c |}{res}        17        160        101        225        134         80        114        152        163        109 {txt}{c |}{res}     1,255 
{txt}       8.5 {c |}{res}        19          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        19 
{txt}         9 {c |}{res}        10        105        110        185        105        114         67        143        100         72 {txt}{c |}{res}     1,011 
{txt}       9.5 {c |}{res}        10          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        10 
{txt}        10 {c |}{res}         3         46         37        110         71         71         30         87         56         57 {txt}{c |}{res}       568 
{txt}      10.5 {c |}{res}         1          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        11 {c |}{res}         7         75         85        205        170        301         32        259        133        138 {txt}{c |}{res}     1,405 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      2,638      1,984      2,804      2,083      2,629      1,942      2,253      2,673      1,315 {txt}{c |}{res}    21,828 

{txt}
{com}. tab PTV country if Timmermans==1

           {txt}{c |}                                                    country
       PTV {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       508        871        650        750        775      1,370        608        566      1,297        365 {txt}{c |}{res}     7,760 
{txt}       1.5 {c |}{res}        51          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        51 
{txt}         2 {c |}{res}       129        262        156        208        151        100        165        150        120         90 {txt}{c |}{res}     1,531 
{txt}       2.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         3 {c |}{res}        77        274        147        201        136         92        153        153        153        105 {txt}{c |}{res}     1,491 
{txt}       3.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         4 {c |}{res}        85        250        153        204        145         95        207        169        123        116 {txt}{c |}{res}     1,547 
{txt}       4.5 {c |}{res}        74          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        74 
{txt}         5 {c |}{res}        78        214        141        148         94         72        161        145        106         70 {txt}{c |}{res}     1,229 
{txt}       5.5 {c |}{res}        58          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        58 
{txt}         6 {c |}{res}       107        240        270        378        202        248        242        276        265        118 {txt}{c |}{res}     2,346 
{txt}       6.5 {c |}{res}        38          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        38 
{txt}         7 {c |}{res}        28        141        134        190        100         86        163        153        157         75 {txt}{c |}{res}     1,227 
{txt}       7.5 {c |}{res}        21          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        21 
{txt}         8 {c |}{res}        17        160        101        225        134         80        114        152        163        109 {txt}{c |}{res}     1,255 
{txt}       8.5 {c |}{res}        19          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        19 
{txt}         9 {c |}{res}        10        105        110        185        105        114         67        143        100         72 {txt}{c |}{res}     1,011 
{txt}       9.5 {c |}{res}        10          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        10 
{txt}        10 {c |}{res}         3         46         37        110         71         71         30         87         56         57 {txt}{c |}{res}       568 
{txt}      10.5 {c |}{res}         1          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        11 {c |}{res}         7         75         85        205        170        301         32        259        133        138 {txt}{c |}{res}     1,405 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      2,638      1,984      2,804      2,083      2,629      1,942      2,253      2,673      1,315 {txt}{c |}{res}    21,828 

{txt}
{com}. tab PTV country if Verhofstadt==1

           {txt}{c |}                                                    country
       PTV {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       508        871        650        750        775      1,370        608        566      1,297        365 {txt}{c |}{res}     7,760 
{txt}       1.5 {c |}{res}        51          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        51 
{txt}         2 {c |}{res}       129        262        156        208        151        100        165        150        120         90 {txt}{c |}{res}     1,531 
{txt}       2.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         3 {c |}{res}        77        274        147        201        136         92        153        153        153        105 {txt}{c |}{res}     1,491 
{txt}       3.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         4 {c |}{res}        85        250        153        204        145         95        207        169        123        116 {txt}{c |}{res}     1,547 
{txt}       4.5 {c |}{res}        74          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        74 
{txt}         5 {c |}{res}        78        214        141        148         94         72        161        145        106         70 {txt}{c |}{res}     1,229 
{txt}       5.5 {c |}{res}        58          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        58 
{txt}         6 {c |}{res}       107        240        270        378        202        248        242        276        265        118 {txt}{c |}{res}     2,346 
{txt}       6.5 {c |}{res}        38          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        38 
{txt}         7 {c |}{res}        28        141        134        190        100         86        163        153        157         75 {txt}{c |}{res}     1,227 
{txt}       7.5 {c |}{res}        21          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        21 
{txt}         8 {c |}{res}        17        160        101        225        134         80        114        152        163        109 {txt}{c |}{res}     1,255 
{txt}       8.5 {c |}{res}        19          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        19 
{txt}         9 {c |}{res}        10        105        110        185        105        114         67        143        100         72 {txt}{c |}{res}     1,011 
{txt}       9.5 {c |}{res}        10          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        10 
{txt}        10 {c |}{res}         3         46         37        110         71         71         30         87         56         57 {txt}{c |}{res}       568 
{txt}      10.5 {c |}{res}         1          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        11 {c |}{res}         7         75         85        205        170        301         32        259        133        138 {txt}{c |}{res}     1,405 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      2,638      1,984      2,804      2,083      2,629      1,942      2,253      2,673      1,315 {txt}{c |}{res}    21,828 

{txt}
{com}. tab PTV country if Vestager==1

           {txt}{c |}                                                    country
       PTV {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       508        871        650        750        775      1,370        608        566      1,297        365 {txt}{c |}{res}     7,760 
{txt}       1.5 {c |}{res}        51          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        51 
{txt}         2 {c |}{res}       129        262        156        208        151        100        165        150        120         90 {txt}{c |}{res}     1,531 
{txt}       2.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         3 {c |}{res}        77        274        147        201        136         92        153        153        153        105 {txt}{c |}{res}     1,491 
{txt}       3.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         4 {c |}{res}        85        250        153        204        145         95        207        169        123        116 {txt}{c |}{res}     1,547 
{txt}       4.5 {c |}{res}        74          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        74 
{txt}         5 {c |}{res}        78        214        141        148         94         72        161        145        106         70 {txt}{c |}{res}     1,229 
{txt}       5.5 {c |}{res}        58          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        58 
{txt}         6 {c |}{res}       107        240        270        378        202        248        242        276        265        118 {txt}{c |}{res}     2,346 
{txt}       6.5 {c |}{res}        38          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        38 
{txt}         7 {c |}{res}        28        141        134        190        100         86        163        153        157         75 {txt}{c |}{res}     1,227 
{txt}       7.5 {c |}{res}        21          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        21 
{txt}         8 {c |}{res}        17        160        101        225        134         80        114        152        163        109 {txt}{c |}{res}     1,255 
{txt}       8.5 {c |}{res}        19          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        19 
{txt}         9 {c |}{res}        10        105        110        185        105        114         67        143        100         72 {txt}{c |}{res}     1,011 
{txt}       9.5 {c |}{res}        10          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        10 
{txt}        10 {c |}{res}         3         46         37        110         71         71         30         87         56         57 {txt}{c |}{res}       568 
{txt}      10.5 {c |}{res}         1          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        11 {c |}{res}         7         75         85        205        170        301         32        259        133        138 {txt}{c |}{res}     1,405 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      2,638      1,984      2,804      2,083      2,629      1,942      2,253      2,673      1,315 {txt}{c |}{res}    21,828 

{txt}
{com}. tab PTV country if Eickhout==1

           {txt}{c |}                                                    country
       PTV {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       508        871        650        750        775      1,370        608        566      1,297        365 {txt}{c |}{res}     7,760 
{txt}       1.5 {c |}{res}        51          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        51 
{txt}         2 {c |}{res}       129        262        156        208        151        100        165        150        120         90 {txt}{c |}{res}     1,531 
{txt}       2.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         3 {c |}{res}        77        274        147        201        136         92        153        153        153        105 {txt}{c |}{res}     1,491 
{txt}       3.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         4 {c |}{res}        85        250        153        204        145         95        207        169        123        116 {txt}{c |}{res}     1,547 
{txt}       4.5 {c |}{res}        74          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        74 
{txt}         5 {c |}{res}        78        214        141        148         94         72        161        145        106         70 {txt}{c |}{res}     1,229 
{txt}       5.5 {c |}{res}        58          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        58 
{txt}         6 {c |}{res}       107        240        270        378        202        248        242        276        265        118 {txt}{c |}{res}     2,346 
{txt}       6.5 {c |}{res}        38          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        38 
{txt}         7 {c |}{res}        28        141        134        190        100         86        163        153        157         75 {txt}{c |}{res}     1,227 
{txt}       7.5 {c |}{res}        21          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        21 
{txt}         8 {c |}{res}        17        160        101        225        134         80        114        152        163        109 {txt}{c |}{res}     1,255 
{txt}       8.5 {c |}{res}        19          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        19 
{txt}         9 {c |}{res}        10        105        110        185        105        114         67        143        100         72 {txt}{c |}{res}     1,011 
{txt}       9.5 {c |}{res}        10          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        10 
{txt}        10 {c |}{res}         3         46         37        110         71         71         30         87         56         57 {txt}{c |}{res}       568 
{txt}      10.5 {c |}{res}         1          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        11 {c |}{res}         7         75         85        205        170        301         32        259        133        138 {txt}{c |}{res}     1,405 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      2,638      1,984      2,804      2,083      2,629      1,942      2,253      2,673      1,315 {txt}{c |}{res}    21,828 

{txt}
{com}. tab PTV country if Keller==1

           {txt}{c |}                                                    country
       PTV {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       508        871        650        750        775      1,370        608        566      1,297        365 {txt}{c |}{res}     7,760 
{txt}       1.5 {c |}{res}        51          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        51 
{txt}         2 {c |}{res}       129        262        156        208        151        100        165        150        120         90 {txt}{c |}{res}     1,531 
{txt}       2.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         3 {c |}{res}        77        274        147        201        136         92        153        153        153        105 {txt}{c |}{res}     1,491 
{txt}       3.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         4 {c |}{res}        85        250        153        204        145         95        207        169        123        116 {txt}{c |}{res}     1,547 
{txt}       4.5 {c |}{res}        74          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        74 
{txt}         5 {c |}{res}        78        214        141        148         94         72        161        145        106         70 {txt}{c |}{res}     1,229 
{txt}       5.5 {c |}{res}        58          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        58 
{txt}         6 {c |}{res}       107        240        270        378        202        248        242        276        265        118 {txt}{c |}{res}     2,346 
{txt}       6.5 {c |}{res}        38          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        38 
{txt}         7 {c |}{res}        28        141        134        190        100         86        163        153        157         75 {txt}{c |}{res}     1,227 
{txt}       7.5 {c |}{res}        21          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        21 
{txt}         8 {c |}{res}        17        160        101        225        134         80        114        152        163        109 {txt}{c |}{res}     1,255 
{txt}       8.5 {c |}{res}        19          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        19 
{txt}         9 {c |}{res}        10        105        110        185        105        114         67        143        100         72 {txt}{c |}{res}     1,011 
{txt}       9.5 {c |}{res}        10          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        10 
{txt}        10 {c |}{res}         3         46         37        110         71         71         30         87         56         57 {txt}{c |}{res}       568 
{txt}      10.5 {c |}{res}         1          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        11 {c |}{res}         7         75         85        205        170        301         32        259        133        138 {txt}{c |}{res}     1,405 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      2,638      1,984      2,804      2,083      2,629      1,942      2,253      2,673      1,315 {txt}{c |}{res}    21,828 

{txt}
{com}. tab PTV country if Zahradil==1

           {txt}{c |}                                                    country
       PTV {c |}   Czechia    Denmark     France    Germany     Greece    Hungary  Netherlan     Poland      Spain     Sweden {c |}     Total
{hline 11}{c +}{hline 110}{c +}{hline 10}
         1 {c |}{res}       508        871        650        750        775      1,370        608        566      1,297        365 {txt}{c |}{res}     7,760 
{txt}       1.5 {c |}{res}        51          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        51 
{txt}         2 {c |}{res}       129        262        156        208        151        100        165        150        120         90 {txt}{c |}{res}     1,531 
{txt}       2.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         3 {c |}{res}        77        274        147        201        136         92        153        153        153        105 {txt}{c |}{res}     1,491 
{txt}       3.5 {c |}{res}        93          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        93 
{txt}         4 {c |}{res}        85        250        153        204        145         95        207        169        123        116 {txt}{c |}{res}     1,547 
{txt}       4.5 {c |}{res}        74          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        74 
{txt}         5 {c |}{res}        78        214        141        148         94         72        161        145        106         70 {txt}{c |}{res}     1,229 
{txt}       5.5 {c |}{res}        58          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        58 
{txt}         6 {c |}{res}       107        240        270        378        202        248        242        276        265        118 {txt}{c |}{res}     2,346 
{txt}       6.5 {c |}{res}        38          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        38 
{txt}         7 {c |}{res}        28        141        134        190        100         86        163        153        157         75 {txt}{c |}{res}     1,227 
{txt}       7.5 {c |}{res}        21          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        21 
{txt}         8 {c |}{res}        17        160        101        225        134         80        114        152        163        109 {txt}{c |}{res}     1,255 
{txt}       8.5 {c |}{res}        19          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        19 
{txt}         9 {c |}{res}        10        105        110        185        105        114         67        143        100         72 {txt}{c |}{res}     1,011 
{txt}       9.5 {c |}{res}        10          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}        10 
{txt}        10 {c |}{res}         3         46         37        110         71         71         30         87         56         57 {txt}{c |}{res}       568 
{txt}      10.5 {c |}{res}         1          0          0          0          0          0          0          0          0          0 {txt}{c |}{res}         1 
{txt}        11 {c |}{res}         7         75         85        205        170        301         32        259        133        138 {txt}{c |}{res}     1,405 
{txt}{hline 11}{c +}{hline 110}{c +}{hline 10}
     Total {c |}{res}     1,507      2,638      1,984      2,804      2,083      2,629      1,942      2,253      2,673      1,315 {txt}{c |}{res}    21,828 

{txt}
{com}. 
. *** Table A5
. sum w5_newsexposure_mean knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration PTV SeveralDomParties OwnCandidate female age education if CandidateRecognition!=. 

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
w5_newsexp~n {c |}{res}    119,189    3.788375    .9076019          1          7
{txt}knowledge_~e {c |}{res}    119,189    3.163094    1.417995          0          5
{txt}{space 1}intefficacy {c |}{res}    119,189    4.302108    1.405551          1          7
{txt}w5_polinte~u {c |}{res}    119,189    4.108122    1.730106          1          7
{txt}turnoutint~5 {c |}{res}    119,189     5.56522    1.985602          1          7
{txt}{hline 13}{c +}{hline 57}
euintegrat~n {c |}{res}    119,189    4.279497     1.72141          1          7
{txt}{space 9}PTV {c |}{res}     94,948     4.10778     3.16415          1         11
{txt}SeveralDom~s {c |}{res}    119,189     .489718    .4998964          0          1
{txt}OwnCandidate {c |}{res}    119,189    .0831453    .2761028          0          1
{txt}{space 6}female {c |}{res}    119,189    .5020849    .4999978          0          1
{txt}{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}    119,189    46.54619    15.40443         17         99
{txt}{space 3}education {c |}{res}    119,189    4.466612    1.814053          1          7
{txt}
{com}. 
. 
. *** Appendix C
. drop u1 u2 groups intercept slope yhat
{txt}
{com}. 
. *** Table A9 - Model 1
. xtmelogit CandidateRecognition w5_newsexposure_mean knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration female age education PTV SeveralDomParties i.candidatename OwnCandidate || country: w5_newsexposure_mean, variance
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-34957.367}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-34843.832}  (not concave)
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-34836.235}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-34836.235}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-34835.651}  (not concave)
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-34828.457}  (not concave)
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-34802.635}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res:-34782.289}  
{res}{txt}Iteration 5:{space 3}log likelihood = {res:-34781.766}  
{res}{txt}Iteration 6:{space 3}log likelihood = {res: -34781.76}  
{res}{txt}Iteration 7:{space 3}log likelihood = {res: -34781.76}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}    94,948
{txt}Group variable: {res}country{col 49}{txt}Number of groups{col 67}={col 70}{res}       10

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}     6,249
{txt}{col 63}avg{col 67}={col 69}{res}   9,494.8
{txt}{col 63}max{col 67}={col 69}{res}    11,904

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}18{txt}){col 67}={col 70}{res} 11904.08
{txt}Log likelihood = {res} -34781.76{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}CandidateRecognition{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
w5_newsexposure_mean {c |}{col 22}{res}{space 2} .2130717{col 34}{space 2} .0453078{col 45}{space 1}    4.70{col 54}{space 3}0.000{col 62}{space 4} .1242701{col 75}{space 3} .3018734
{txt}{space 4}knowledge_stable {c |}{col 22}{res}{space 2} .1743901{col 34}{space 2} .0084167{col 45}{space 1}   20.72{col 54}{space 3}0.000{col 62}{space 4} .1578937{col 75}{space 3} .1908864
{txt}{space 9}intefficacy {c |}{col 22}{res}{space 2} .1231597{col 34}{space 2}  .009065{col 45}{space 1}   13.59{col 54}{space 3}0.000{col 62}{space 4} .1053927{col 75}{space 3} .1409267
{txt}{space 3}w5_polinterest_eu {c |}{col 22}{res}{space 2}  .266367{col 34}{space 2} .0077145{col 45}{space 1}   34.53{col 54}{space 3}0.000{col 62}{space 4} .2512468{col 75}{space 3} .2814873
{txt}{space 1}turnoutintention_w5 {c |}{col 22}{res}{space 2} .0212798{col 34}{space 2} .0059076{col 45}{space 1}    3.60{col 54}{space 3}0.000{col 62}{space 4} .0097011{col 75}{space 3} .0328586
{txt}{space 7}euintegration {c |}{col 22}{res}{space 2} .0495551{col 34}{space 2} .0060197{col 45}{space 1}    8.23{col 54}{space 3}0.000{col 62}{space 4} .0377567{col 75}{space 3} .0613535
{txt}{space 14}female {c |}{col 22}{res}{space 2}-.1280428{col 34}{space 2} .0198591{col 45}{space 1}   -6.45{col 54}{space 3}0.000{col 62}{space 4}-.1669661{col 75}{space 3}-.0891196
{txt}{space 17}age {c |}{col 22}{res}{space 2} .0031882{col 34}{space 2} .0006706{col 45}{space 1}    4.75{col 54}{space 3}0.000{col 62}{space 4} .0018738{col 75}{space 3} .0045026
{txt}{space 11}education {c |}{col 22}{res}{space 2} .0464184{col 34}{space 2}  .005706{col 45}{space 1}    8.14{col 54}{space 3}0.000{col 62}{space 4} .0352349{col 75}{space 3} .0576019
{txt}{space 17}PTV {c |}{col 22}{res}{space 2} .0225077{col 34}{space 2} .0030623{col 45}{space 1}    7.35{col 54}{space 3}0.000{col 62}{space 4} .0165058{col 75}{space 3} .0285097
{txt}{space 3}SeveralDomParties {c |}{col 22}{res}{space 2} .2538986{col 34}{space 2} .0287242{col 45}{space 1}    8.84{col 54}{space 3}0.000{col 62}{space 4} .1976001{col 75}{space 3}  .310197
{txt}{space 20} {c |}
{space 7}candidatename {c |}
{space 9}Timmermans  {c |}{col 22}{res}{space 2} .5242376{col 34}{space 2} .0277553{col 45}{space 1}   18.89{col 54}{space 3}0.000{col 62}{space 4} .4698382{col 75}{space 3} .5786371
{txt}{space 8}Verhofstadt  {c |}{col 22}{res}{space 2}  -.02858{col 34}{space 2} .0342248{col 45}{space 1}   -0.84{col 54}{space 3}0.404{col 62}{space 4}-.0956594{col 75}{space 3} .0384993
{txt}{space 11}Vestager  {c |}{col 22}{res}{space 2}-.2074834{col 34}{space 2} .0344338{col 45}{space 1}   -6.03{col 54}{space 3}0.000{col 62}{space 4}-.2749723{col 75}{space 3}-.1399945
{txt}{space 11}Eickhout  {c |}{col 22}{res}{space 2}-2.096149{col 34}{space 2} .0506465{col 45}{space 1}  -41.39{col 54}{space 3}0.000{col 62}{space 4}-2.195414{col 75}{space 3}-1.996884
{txt}{space 13}Keller  {c |}{col 22}{res}{space 2}-1.652722{col 34}{space 2}  .045408{col 45}{space 1}  -36.40{col 54}{space 3}0.000{col 62}{space 4}-1.741721{col 75}{space 3}-1.563724
{txt}{space 11}Zahradil  {c |}{col 22}{res}{space 2}-1.127755{col 34}{space 2} .0378858{col 45}{space 1}  -29.77{col 54}{space 3}0.000{col 62}{space 4} -1.20201{col 75}{space 3}  -1.0535
{txt}{space 20} {c |}
{space 8}OwnCandidate {c |}{col 22}{res}{space 2} 2.509758{col 34}{space 2}  .033149{col 45}{space 1}   75.71{col 54}{space 3}0.000{col 62}{space 4} 2.444788{col 75}{space 3} 2.574729
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-5.493992{col 34}{space 2} .3429353{col 45}{space 1}  -16.02{col 54}{space 3}0.000{col 62}{space 4}-6.166133{col 75}{space 3}-4.821851
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}country{txt}: Independent{col 30}{c |}
{col 16}var(w5_new~n){col 30}{c |}{res}{col 33} .0188744{col 44} .0091624{col 58} .0072889{col 70} .0488743
{txt}{col 19}var(_cons){col 30}{c |}{res}{col 33} 1.111798{col 44} .5098829{col 58}  .452538{col 70} 2.731469
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}chi2({res}2{txt}) = {res}3708.53{col 59}{txt}Prob > chi2 ={col 73}{res}0.0000

{txt}{p 0 6 4}Note: {help j_mixedlr##|_new:LR test is conservative} and provided only for reference.{p_end}

{com}. 
. 
. *** Figure A1
. 
. predict u*, reffects
{txt}
{com}. bysort country: generate groups=(_n==1) 
{txt}
{com}. list country u2 u1 if groups
{txt}
        {c TLC}{hline 13}{c -}{hline 11}{c -}{hline 11}{c TRC}
        {c |} {res}    country          u2          u1 {txt}{c |}
        {c LT}{hline 13}{c -}{hline 11}{c -}{hline 11}{c RT}
     1. {c |} {res}    Czechia    .7921689   -.1074312 {txt}{c |}
 10550. {c |} {res}    Denmark    .7878774    -.145706 {txt}{c |}
 29296. {c |} {res}     France   -1.606019    .2256221 {txt}{c |}
 43184. {c |} {res}    Germany   -.8623477   -.0294296 {txt}{c |}
 63449. {c |} {res}     Greece    .2224524    -.028807 {txt}{c |}
        {c LT}{hline 13}{c -}{hline 11}{c -}{hline 11}{c RT}
 78030. {c |} {res}    Hungary    .8657653   -.0085497 {txt}{c |}
 97252. {c |} {res}Netherlands    .6658061   -.0914412 {txt}{c |}
118434. {c |} {res}     Poland    1.510362   -.1483135 {txt}{c |}
134205. {c |} {res}      Spain   -1.226763    .2121509 {txt}{c |}
154274. {c |} {res}     Sweden   -1.134808    .1192537 {txt}{c |}
        {c BLC}{hline 13}{c -}{hline 11}{c -}{hline 11}{c BRC}

{com}. gen intercept = _b[_cons] + u2
{txt}
{com}. gen slope = _b[w5_newsexposure_mean] + u1
{txt}
{com}. gen yhat= intercept + (slope*w5_newsexposure_mean)
{txt}(44,289 missing values generated)

{com}. 
. twoway connected yhat w5_newsexposure_mean, connect(L) by(country)
{res}{txt}
{com}. 
. 
. *** Table A9 - Model 2
. xtmelogit CandidateRecognition c.w5_newsexposure_mean##OwnCandidate knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration female age education PTV SeveralDomParties i.candidatename || country: , variance
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -34965.98}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-34896.833}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-34839.398}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-34839.398}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-34798.105}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-34796.906}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-34796.875}  
{res}{txt}Iteration 4:{space 3}log likelihood = {res:-34796.875}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}    94,948
{txt}Group variable: {res}country{col 49}{txt}Number of groups{col 67}={col 70}{res}       10

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}     6,249
{txt}{col 63}avg{col 67}={col 69}{res}   9,494.8
{txt}{col 63}max{col 67}={col 69}{res}    11,904

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}19{txt}){col 67}={col 70}{res} 12565.54
{txt}Log likelihood = {res}-34796.875{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 36}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               CandidateRecognition{col 37}{c |}      Coef.{col 49}   Std. Err.{col 61}      z{col 69}   P>|z|{col 77}     [95% Con{col 90}f. Interval]
{hline 36}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}w5_newsexposure_mean {c |}{col 37}{res}{space 2} .2366541{col 49}{space 2}  .013082{col 60}{space 1}   18.09{col 69}{space 3}0.000{col 77}{space 4} .2110139{col 90}{space 3} .2622944
{txt}{space 21}1.OwnCandidate {c |}{col 37}{res}{space 2} 3.547397{col 49}{space 2} .1291139{col 60}{space 1}   27.47{col 69}{space 3}0.000{col 77}{space 4} 3.294339{col 90}{space 3} 3.800456
{txt}{space 35} {c |}
OwnCandidate#c.w5_newsexposure_mean {c |}
{space 33}1  {c |}{col 37}{res}{space 2}-.2704484{col 49}{space 2} .0327889{col 60}{space 1}   -8.25{col 69}{space 3}0.000{col 77}{space 4}-.3347135{col 90}{space 3}-.2061833
{txt}{space 35} {c |}
{space 19}knowledge_stable {c |}{col 37}{res}{space 2} .1735931{col 49}{space 2} .0084055{col 60}{space 1}   20.65{col 69}{space 3}0.000{col 77}{space 4} .1571186{col 90}{space 3} .1900677
{txt}{space 24}intefficacy {c |}{col 37}{res}{space 2} .1225064{col 49}{space 2} .0090564{col 60}{space 1}   13.53{col 69}{space 3}0.000{col 77}{space 4} .1047561{col 90}{space 3} .1402566
{txt}{space 18}w5_polinterest_eu {c |}{col 37}{res}{space 2} .2674538{col 49}{space 2} .0077053{col 60}{space 1}   34.71{col 69}{space 3}0.000{col 77}{space 4} .2523517{col 90}{space 3} .2825559
{txt}{space 16}turnoutintention_w5 {c |}{col 37}{res}{space 2} .0212346{col 49}{space 2} .0059001{col 60}{space 1}    3.60{col 69}{space 3}0.000{col 77}{space 4} .0096706{col 90}{space 3} .0327986
{txt}{space 22}euintegration {c |}{col 37}{res}{space 2} .0498242{col 49}{space 2} .0060058{col 60}{space 1}    8.30{col 69}{space 3}0.000{col 77}{space 4}  .038053{col 90}{space 3} .0615954
{txt}{space 29}female {c |}{col 37}{res}{space 2}-.1305793{col 49}{space 2} .0198429{col 60}{space 1}   -6.58{col 69}{space 3}0.000{col 77}{space 4}-.1694706{col 90}{space 3} -.091688
{txt}{space 32}age {c |}{col 37}{res}{space 2} .0029288{col 49}{space 2} .0006695{col 60}{space 1}    4.37{col 69}{space 3}0.000{col 77}{space 4} .0016167{col 90}{space 3} .0042409
{txt}{space 26}education {c |}{col 37}{res}{space 2} .0469964{col 49}{space 2} .0056997{col 60}{space 1}    8.25{col 69}{space 3}0.000{col 77}{space 4} .0358253{col 90}{space 3} .0581676
{txt}{space 32}PTV {c |}{col 37}{res}{space 2} .0229715{col 49}{space 2}   .00306{col 60}{space 1}    7.51{col 69}{space 3}0.000{col 77}{space 4} .0169741{col 90}{space 3}  .028969
{txt}{space 18}SeveralDomParties {c |}{col 37}{res}{space 2} .2573219{col 49}{space 2}  .028691{col 60}{space 1}    8.97{col 69}{space 3}0.000{col 77}{space 4} .2010886{col 90}{space 3} .3135552
{txt}{space 35} {c |}
{space 22}candidatename {c |}
{space 24}Timmermans  {c |}{col 37}{res}{space 2} .5229802{col 49}{space 2} .0277625{col 60}{space 1}   18.84{col 69}{space 3}0.000{col 77}{space 4} .4685666{col 90}{space 3} .5773938
{txt}{space 23}Verhofstadt  {c |}{col 37}{res}{space 2}-.0306803{col 49}{space 2} .0342007{col 60}{space 1}   -0.90{col 69}{space 3}0.370{col 77}{space 4}-.0977124{col 90}{space 3} .0363517
{txt}{space 26}Vestager  {c |}{col 37}{res}{space 2}-.2093984{col 49}{space 2}   .03438{col 60}{space 1}   -6.09{col 69}{space 3}0.000{col 77}{space 4} -.276782{col 90}{space 3}-.1420149
{txt}{space 26}Eickhout  {c |}{col 37}{res}{space 2}-2.089622{col 49}{space 2} .0505311{col 60}{space 1}  -41.35{col 69}{space 3}0.000{col 77}{space 4}-2.188661{col 90}{space 3}-1.990583
{txt}{space 28}Keller  {c |}{col 37}{res}{space 2}-1.640867{col 49}{space 2} .0452725{col 60}{space 1}  -36.24{col 69}{space 3}0.000{col 77}{space 4}-1.729599{col 90}{space 3}-1.552134
{txt}{space 26}Zahradil  {c |}{col 37}{res}{space 2}-1.139301{col 49}{space 2} .0379759{col 60}{space 1}  -30.00{col 69}{space 3}0.000{col 77}{space 4}-1.213733{col 90}{space 3} -1.06487
{txt}{space 35} {c |}
{space 30}_cons {c |}{col 37}{res}{space 2}-5.567647{col 49}{space 2} .2102865{col 60}{space 1}  -26.48{col 69}{space 3}0.000{col 77}{space 4}-5.979801{col 90}{space 3}-5.155493
{txt}{hline 36}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}country{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .3771674{col 44} .1691884{col 58} .1565688{col 70} .9085799
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}{help j_chibar##|_new:chibar2(01) =} {res}3606.75{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. 
. *** Table A9 - Model 3
. xtmelogit CandidateRecognition c.w5_newsexposure_mean##OwnCandidate knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration female age education SeveralDomParties i.candidatename || country: , variance
{res}
{txt}Refining starting values: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res:-44342.124}  (not concave)
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-44221.052}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res: -44202.57}  
{res}
{txt}Performing gradient-based optimization: 
{res}
{txt}Iteration 0:{space 3}log likelihood = {res: -44202.57}  
{res}{txt}Iteration 1:{space 3}log likelihood = {res:-44195.825}  
{res}{txt}Iteration 2:{space 3}log likelihood = {res:-44195.803}  
{res}{txt}Iteration 3:{space 3}log likelihood = {res:-44195.803}  
{res}
{txt}Mixed-effects logistic regression{col 49}Number of obs{col 67}={col 69}{res}   119,189
{txt}Group variable: {res}country{col 49}{txt}Number of groups{col 67}={col 70}{res}       10

{txt}{col 49}Obs per group:
{col 63}min{col 67}={col 69}{res}     9,023
{txt}{col 63}avg{col 67}={col 69}{res}  11,918.9
{txt}{col 63}max{col 67}={col 69}{res}    15,771

{txt}Integration points = {res}  7{col 49}{txt}Wald chi2({res}18{txt}){col 67}={col 70}{res} 15006.23
{txt}Log likelihood = {res}-44195.803{col 49}{txt}Prob > chi2{col 67}={col 73}{res}0.0000

{txt}{hline 36}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}               CandidateRecognition{col 37}{c |}      Coef.{col 49}   Std. Err.{col 61}      z{col 69}   P>|z|{col 77}     [95% Con{col 90}f. Interval]
{hline 36}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}w5_newsexposure_mean {c |}{col 37}{res}{space 2} .2410319{col 49}{space 2} .0111688{col 60}{space 1}   21.58{col 69}{space 3}0.000{col 77}{space 4} .2191414{col 90}{space 3} .2629223
{txt}{space 21}1.OwnCandidate {c |}{col 37}{res}{space 2} 3.453058{col 49}{space 2} .1229632{col 60}{space 1}   28.08{col 69}{space 3}0.000{col 77}{space 4} 3.212054{col 90}{space 3} 3.694061
{txt}{space 35} {c |}
OwnCandidate#c.w5_newsexposure_mean {c |}
{space 33}1  {c |}{col 37}{res}{space 2}-.2716579{col 49}{space 2} .0312701{col 60}{space 1}   -8.69{col 69}{space 3}0.000{col 77}{space 4}-.3329462{col 90}{space 3}-.2103697
{txt}{space 35} {c |}
{space 19}knowledge_stable {c |}{col 37}{res}{space 2} .1565209{col 49}{space 2} .0073853{col 60}{space 1}   21.19{col 69}{space 3}0.000{col 77}{space 4}  .142046{col 90}{space 3} .1709958
{txt}{space 24}intefficacy {c |}{col 37}{res}{space 2} .1169201{col 49}{space 2} .0080271{col 60}{space 1}   14.57{col 69}{space 3}0.000{col 77}{space 4} .1011872{col 90}{space 3}  .132653
{txt}{space 18}w5_polinterest_eu {c |}{col 37}{res}{space 2} .2733823{col 49}{space 2} .0068341{col 60}{space 1}   40.00{col 69}{space 3}0.000{col 77}{space 4} .2599878{col 90}{space 3} .2867768
{txt}{space 16}turnoutintention_w5 {c |}{col 37}{res}{space 2}  .010452{col 49}{space 2} .0052515{col 60}{space 1}    1.99{col 69}{space 3}0.047{col 77}{space 4} .0001592{col 90}{space 3} .0207449
{txt}{space 22}euintegration {c |}{col 37}{res}{space 2} .0473566{col 49}{space 2} .0052407{col 60}{space 1}    9.04{col 69}{space 3}0.000{col 77}{space 4} .0370851{col 90}{space 3}  .057628
{txt}{space 29}female {c |}{col 37}{res}{space 2}-.0915172{col 49}{space 2} .0176091{col 60}{space 1}   -5.20{col 69}{space 3}0.000{col 77}{space 4}-.1260305{col 90}{space 3}-.0570039
{txt}{space 32}age {c |}{col 37}{res}{space 2}-.0003065{col 49}{space 2} .0006036{col 60}{space 1}   -0.51{col 69}{space 3}0.612{col 77}{space 4}-.0014895{col 90}{space 3} .0008764
{txt}{space 26}education {c |}{col 37}{res}{space 2} .0380916{col 49}{space 2} .0050952{col 60}{space 1}    7.48{col 69}{space 3}0.000{col 77}{space 4} .0281052{col 90}{space 3}  .048078
{txt}{space 18}SeveralDomParties {c |}{col 37}{res}{space 2} .3495525{col 49}{space 2} .0206851{col 60}{space 1}   16.90{col 69}{space 3}0.000{col 77}{space 4} .3090105{col 90}{space 3} .3900945
{txt}{space 35} {c |}
{space 22}candidatename {c |}
{space 24}Timmermans  {c |}{col 37}{res}{space 2} .5245341{col 49}{space 2}  .027074{col 60}{space 1}   19.37{col 69}{space 3}0.000{col 77}{space 4}   .47147{col 90}{space 3} .5775983
{txt}{space 23}Verhofstadt  {c |}{col 37}{res}{space 2}-.1004736{col 49}{space 2}  .028669{col 60}{space 1}   -3.50{col 69}{space 3}0.000{col 77}{space 4}-.1566637{col 90}{space 3}-.0442834
{txt}{space 26}Vestager  {c |}{col 37}{res}{space 2}-.3128689{col 49}{space 2} .0290166{col 60}{space 1}  -10.78{col 69}{space 3}0.000{col 77}{space 4}-.3697402{col 90}{space 3}-.2559975
{txt}{space 26}Eickhout  {c |}{col 37}{res}{space 2}-1.771008{col 49}{space 2}  .040056{col 60}{space 1}  -44.21{col 69}{space 3}0.000{col 77}{space 4}-1.849516{col 90}{space 3}  -1.6925
{txt}{space 28}Keller  {c |}{col 37}{res}{space 2}-1.351851{col 49}{space 2} .0362004{col 60}{space 1}  -37.34{col 69}{space 3}0.000{col 77}{space 4}-1.422803{col 90}{space 3}-1.280899
{txt}{space 26}Zahradil  {c |}{col 37}{res}{space 2}-1.116015{col 49}{space 2} .0337951{col 60}{space 1}  -33.02{col 69}{space 3}0.000{col 77}{space 4}-1.182252{col 90}{space 3}-1.049778
{txt}{space 35} {c |}
{space 30}_cons {c |}{col 37}{res}{space 2}-5.218249{col 49}{space 2} .1958687{col 60}{space 1}  -26.64{col 69}{space 3}0.000{col 77}{space 4}-5.602144{col 90}{space 3}-4.834353
{txt}{hline 36}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{hline 29}{c TT}{hline 48}
{col 3}Random-effects Parameters{col 30}{c |}{col 34}Estimate{col 45}Std. Err.{col 59}[95% Conf. Interval]
{hline 29}{c +}{hline 48}
{res}country{txt}: Identity{col 30}{c |}
{col 19}var(_cons){col 30}{c |}{res}{col 33} .3354737{col 44} .1504581{col 58} .1392834{col 70} .8080116
{txt}{hline 29}{c BT}{hline 48}
LR test vs. logistic model:{col 29}{help j_chibar##|_new:chibar2(01) =} {res}4178.05{col 55}{txt}Prob >= chibar2 = {col 73}{res}0.0000
{txt}
{com}. 
. 
. *** Table A10
. mean n_cand_rec, over(country) 
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}   119,189

      {txt}Czechia: country = {res}Czechia
      {txt}Denmark: country = {res}Denmark
       {txt}France: country = {res}France
      {txt}Germany: country = {res}Germany
       {txt}Greece: country = {res}Greece
      {txt}Hungary: country = {res}Hungary
  {txt}Netherlands: country = {res}Netherlands
       {txt}Poland: country = {res}Poland
        {txt}Spain: country = {res}Spain
       {txt}Sweden: country = {res}Sweden

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}        Over{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}n_cand_rec   {txt}{c |}
{space 5}Czechia {c |}{col 14}{res}{space 2} 1.225614{col 26}{space 2} .0141868{col 37}{space 5} 1.197808{col 51}{space 3}  1.25342
{txt}{space 5}Denmark {c |}{col 14}{res}{space 2} 1.541999{col 26}{space 2} .0103612{col 37}{space 5} 1.521691{col 51}{space 3} 1.562306
{txt}{space 6}France {c |}{col 14}{res}{space 2} .5720766{col 26}{space 2} .0107732{col 37}{space 5} .5509614{col 51}{space 3} .5931918
{txt}{space 5}Germany {c |}{col 14}{res}{space 2} 1.105203{col 26}{space 2} .0141988{col 37}{space 5} 1.077374{col 51}{space 3} 1.133032
{txt}{space 6}Greece {c |}{col 14}{res}{space 2} 1.200672{col 26}{space 2} .0133469{col 37}{space 5} 1.174512{col 51}{space 3} 1.226832
{txt}{space 5}Hungary {c |}{col 14}{res}{space 2} 1.944143{col 26}{space 2} .0179619{col 37}{space 5} 1.908938{col 51}{space 3} 1.979348
{txt}{space 1}Netherlands {c |}{col 14}{res}{space 2} 1.661576{col 26}{space 2} .0119945{col 37}{space 5} 1.638067{col 51}{space 3} 1.685085
{txt}{space 6}Poland {c |}{col 14}{res}{space 2} 1.918775{col 26}{space 2} .0138161{col 37}{space 5} 1.891696{col 51}{space 3} 1.945854
{txt}{space 7}Spain {c |}{col 14}{res}{space 2} .9075364{col 26}{space 2} .0146834{col 37}{space 5} .8787572{col 51}{space 3} .9363157
{txt}{space 6}Sweden {c |}{col 14}{res}{space 2} .5901141{col 26}{space 2} .0118726{col 37}{space 5} .5668439{col 51}{space 3} .6133843
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. 
. *** Table A11
. mean n_foreign_can_rec_mean, over(country) 
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}   119,189

      {txt}Czechia: country = {res}Czechia
      {txt}Denmark: country = {res}Denmark
       {txt}France: country = {res}France
      {txt}Germany: country = {res}Germany
       {txt}Greece: country = {res}Greece
      {txt}Hungary: country = {res}Hungary
  {txt}Netherlands: country = {res}Netherlands
       {txt}Poland: country = {res}Poland
        {txt}Spain: country = {res}Spain
       {txt}Sweden: country = {res}Sweden

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}                  Over{col 24}{c |}       Mean{col 36}   Std. Err.{col 48}     [95% Con{col 61}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}n_foreign_can_rec_mean {txt}{c |}
{space 15}Czechia {c |}{col 24}{res}{space 2} .1166777{col 36}{space 2} .0019716{col 47}{space 5} .1128134{col 61}{space 3}  .120542
{txt}{space 15}Denmark {c |}{col 24}{res}{space 2} .1029021{col 36}{space 2} .0016256{col 47}{space 5}  .099716{col 61}{space 3} .1060882
{txt}{space 16}France {c |}{col 24}{res}{space 2} .0817252{col 36}{space 2}  .001539{col 47}{space 5} .0787088{col 61}{space 3} .0847417
{txt}{space 15}Germany {c |}{col 24}{res}{space 2} .1215552{col 36}{space 2} .0019641{col 47}{space 5} .1177056{col 61}{space 3} .1254048
{txt}{space 16}Greece {c |}{col 24}{res}{space 2} .1715246{col 36}{space 2} .0019067{col 47}{space 5} .1677875{col 61}{space 3} .1752617
{txt}{space 15}Hungary {c |}{col 24}{res}{space 2} .2777347{col 36}{space 2}  .002566{col 47}{space 5} .2727054{col 61}{space 3}  .282764
{txt}{space 11}Netherlands {c |}{col 24}{res}{space 2} .1626145{col 36}{space 2} .0016935{col 47}{space 5} .1592953{col 61}{space 3} .1659337
{txt}{space 16}Poland {c |}{col 24}{res}{space 2} .2741107{col 36}{space 2} .0019737{col 47}{space 5} .2702422{col 61}{space 3} .2779792
{txt}{space 17}Spain {c |}{col 24}{res}{space 2} .1296481{col 36}{space 2} .0020976{col 47}{space 5} .1255367{col 61}{space 3} .1337594
{txt}{space 16}Sweden {c |}{col 24}{res}{space 2}  .084302{col 36}{space 2} .0016961{col 47}{space 5} .0809777{col 61}{space 3} .0876263
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. 
. *** Table A12
. * generating alternative OwnCandidate variable
. tab country

    {txt}country {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
    Czechia {c |}{res}     10,549        6.45        6.45
{txt}    Denmark {c |}{res}     18,746       11.47       17.92
{txt}     France {c |}{res}     13,888        8.50       26.42
{txt}    Germany {c |}{res}     20,265       12.40       38.81
{txt}     Greece {c |}{res}     14,581        8.92       47.73
{txt}    Hungary {c |}{res}     19,222       11.76       59.49
{txt}Netherlands {c |}{res}     21,182       12.96       72.45
{txt}     Poland {c |}{res}     15,771        9.65       82.09
{txt}      Spain {c |}{res}     20,069       12.28       94.37
{txt}     Sweden {c |}{res}      9,205        5.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    163,478      100.00
{txt}
{com}. tab country, nolabel

    {txt}country {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}     10,549        6.45        6.45
{txt}          2 {c |}{res}     18,746       11.47       17.92
{txt}          3 {c |}{res}     13,888        8.50       26.42
{txt}          4 {c |}{res}     20,265       12.40       38.81
{txt}          5 {c |}{res}     14,581        8.92       47.73
{txt}          6 {c |}{res}     19,222       11.76       59.49
{txt}          7 {c |}{res}     21,182       12.96       72.45
{txt}          8 {c |}{res}     15,771        9.65       82.09
{txt}          9 {c |}{res}     20,069       12.28       94.37
{txt}         10 {c |}{res}      9,205        5.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    163,478      100.00
{txt}
{com}. 
. gen OwnCandidate2=0
{txt}
{com}. replace OwnCandidate2=1 if country==1
{txt}(10,549 real changes made)

{com}. replace OwnCandidate2=1 if country==2
{txt}(18,746 real changes made)

{com}. replace OwnCandidate2=1 if country==4
{txt}(20,265 real changes made)

{com}. replace OwnCandidate2=1 if country==7
{txt}(21,182 real changes made)

{com}. 
. tab OwnCandidate2

{txt}OwnCandidat {c |}
         e2 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}     92,736       56.73       56.73
{txt}          1 {c |}{res}     70,742       43.27      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}    163,478      100.00
{txt}
{com}. 
. * comparing distributions of the DV in stacked and non-stcaked data
. 
. sum n_cand_rec

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}n_cand_rec {c |}{res}    119,189      1.2738    1.543631          0          7
{txt}
{com}. sum n_cand_rec if Weber==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}n_cand_rec {c |}{res}     17,027      1.2738     1.54367          0          7
{txt}
{com}. sum n_cand_rec if Vestager==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}n_cand_rec {c |}{res}     17,027      1.2738     1.54367          0          7
{txt}
{com}. 
. * fitting negative binomial regression model
. nbreg n_cand_rec w5_newsexposure_mean knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration female age education OwnCandidate2 i.country, cluster(ID) nolog
{txt}note: 10.country omitted because of collinearity
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}   119,189
{txt}{col 49}Wald chi2({res}18{txt}){col 67}= {res}   5066.02
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-167568.33{txt}{col 49}Pseudo R2{col 67}= {res}    0.0976

{txt}{ralign 86:(Std. Err. adjusted for {res:17,027} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}          n_cand_rec{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
w5_newsexposure_mean {c |}{col 22}{res}{space 2} .1402867{col 34}{space 2}  .011775{col 45}{space 1}   11.91{col 54}{space 3}0.000{col 62}{space 4} .1172081{col 75}{space 3} .1633654
{txt}{space 4}knowledge_stable {c |}{col 22}{res}{space 2} .1078541{col 34}{space 2} .0081747{col 45}{space 1}   13.19{col 54}{space 3}0.000{col 62}{space 4} .0918319{col 75}{space 3} .1238763
{txt}{space 9}intefficacy {c |}{col 22}{res}{space 2}   .07596{col 34}{space 2}  .008231{col 45}{space 1}    9.23{col 54}{space 3}0.000{col 62}{space 4} .0598276{col 75}{space 3} .0920924
{txt}{space 3}w5_polinterest_eu {c |}{col 22}{res}{space 2} .1901404{col 34}{space 2} .0073038{col 45}{space 1}   26.03{col 54}{space 3}0.000{col 62}{space 4} .1758253{col 75}{space 3} .2044555
{txt}{space 1}turnoutintention_w5 {c |}{col 22}{res}{space 2} .0100461{col 34}{space 2} .0055729{col 45}{space 1}    1.80{col 54}{space 3}0.071{col 62}{space 4}-.0008765{col 75}{space 3} .0209687
{txt}{space 7}euintegration {c |}{col 22}{res}{space 2} .0311542{col 34}{space 2} .0053213{col 45}{space 1}    5.85{col 54}{space 3}0.000{col 62}{space 4} .0207247{col 75}{space 3} .0415837
{txt}{space 14}female {c |}{col 22}{res}{space 2}-.0658715{col 34}{space 2} .0179701{col 45}{space 1}   -3.67{col 54}{space 3}0.000{col 62}{space 4}-.1010923{col 75}{space 3}-.0306507
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.0006475{col 34}{space 2} .0006051{col 45}{space 1}   -1.07{col 54}{space 3}0.285{col 62}{space 4}-.0018334{col 75}{space 3} .0005384
{txt}{space 11}education {c |}{col 22}{res}{space 2} .0204306{col 34}{space 2} .0051603{col 45}{space 1}    3.96{col 54}{space 3}0.000{col 62}{space 4} .0103166{col 75}{space 3} .0305447
{txt}{space 7}OwnCandidate2 {c |}{col 22}{res}{space 2} .9737632{col 34}{space 2} .0575575{col 45}{space 1}   16.92{col 54}{space 3}0.000{col 62}{space 4} .8609525{col 75}{space 3} 1.086574
{txt}{space 20} {c |}
{space 13}country {c |}
{space 12}Denmark  {c |}{col 22}{res}{space 2} .0659369{col 34}{space 2}  .035073{col 45}{space 1}    1.88{col 54}{space 3}0.060{col 62}{space 4} -.002805{col 75}{space 3} .1346788
{txt}{space 13}France  {c |}{col 22}{res}{space 2} .0204444{col 34}{space 2} .0675752{col 45}{space 1}    0.30{col 54}{space 3}0.762{col 62}{space 4}-.1120005{col 75}{space 3} .1528893
{txt}{space 12}Germany  {c |}{col 22}{res}{space 2}-.4640752{col 34}{space 2} .0436517{col 45}{space 1}  -10.63{col 54}{space 3}0.000{col 62}{space 4}-.5496309{col 75}{space 3}-.3785194
{txt}{space 13}Greece  {c |}{col 22}{res}{space 2} .5447488{col 34}{space 2} .0580519{col 45}{space 1}    9.38{col 54}{space 3}0.000{col 62}{space 4} .4309692{col 75}{space 3} .6585285
{txt}{space 12}Hungary  {c |}{col 22}{res}{space 2} 1.055242{col 34}{space 2} .0550283{col 45}{space 1}   19.18{col 54}{space 3}0.000{col 62}{space 4}  .947388{col 75}{space 3} 1.163095
{txt}{space 8}Netherlands  {c |}{col 22}{res}{space 2} .2921097{col 34}{space 2} .0345903{col 45}{space 1}    8.44{col 54}{space 3}0.000{col 62}{space 4} .2243139{col 75}{space 3} .3599054
{txt}{space 13}Poland  {c |}{col 22}{res}{space 2} 1.039014{col 34}{space 2} .0530908{col 45}{space 1}   19.57{col 54}{space 3}0.000{col 62}{space 4} .9349576{col 75}{space 3}  1.14307
{txt}{space 14}Spain  {c |}{col 22}{res}{space 2} .2277668{col 34}{space 2} .0632778{col 45}{space 1}    3.60{col 54}{space 3}0.000{col 62}{space 4} .1037447{col 75}{space 3}  .351789
{txt}{space 13}Sweden  {c |}{col 22}{res}{space 2}        0{col 34}{txt}  (omitted)
{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2}-2.842419{col 34}{space 2} .0848007{col 45}{space 1}  -33.52{col 54}{space 3}0.000{col 62}{space 4}-3.008625{col 75}{space 3}-2.676213
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2}-1.218223{col 34}{space 2} .0519288{col 62}{space 4}-1.320002{col 75}{space 3}-1.116445
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} .2957552{col 34}{space 2} .0153582{col 62}{space 4} .2671349{col 75}{space 3} .3274419
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. nbreg n_cand_rec c.w5_newsexposure_mean##country knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration female age education OwnCandidate2, cluster(ID) nolog
{txt}note: OwnCandidate2 omitted because of collinearity
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}   119,189
{txt}{col 49}Wald chi2({res}27{txt}){col 67}= {res}   4922.49
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-167104.12{txt}{col 49}Pseudo R2{col 67}= {res}    0.1001

{txt}{ralign 96:(Std. Err. adjusted for {res:17,027} clusters in ID)}
{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    n_cand_rec{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}w5_newsexposure_mean {c |}{col 32}{res}{space 2} .0816079{col 44}{space 2} .0372737{col 55}{space 1}    2.19{col 64}{space 3}0.029{col 72}{space 4} .0085528{col 85}{space 3}  .154663
{txt}{space 30} {c |}
{space 23}country {c |}
{space 22}Denmark  {c |}{col 32}{res}{space 2} .3629915{col 44}{space 2} .1834923{col 55}{space 1}    1.98{col 64}{space 3}0.048{col 72}{space 4} .0033532{col 85}{space 3} .7226298
{txt}{space 23}France  {c |}{col 32}{res}{space 2}-2.227001{col 44}{space 2} .3028421{col 55}{space 1}   -7.35{col 64}{space 3}0.000{col 72}{space 4}-2.820561{col 85}{space 3}-1.633442
{txt}{space 22}Germany  {c |}{col 32}{res}{space 2}-.6005971{col 44}{space 2} .2321907{col 55}{space 1}   -2.59{col 64}{space 3}0.010{col 72}{space 4}-1.055683{col 85}{space 3}-.1455116
{txt}{space 23}Greece  {c |}{col 32}{res}{space 2}-.7866012{col 44}{space 2} .1888233{col 55}{space 1}   -4.17{col 64}{space 3}0.000{col 72}{space 4}-1.156688{col 85}{space 3}-.4165144
{txt}{space 22}Hungary  {c |}{col 32}{res}{space 2}-.0539455{col 44}{space 2} .1743893{col 55}{space 1}   -0.31{col 64}{space 3}0.757{col 72}{space 4}-.3957422{col 85}{space 3} .2878511
{txt}{space 18}Netherlands  {c |}{col 32}{res}{space 2} .5026308{col 44}{space 2} .1824868{col 55}{space 1}    2.75{col 64}{space 3}0.006{col 72}{space 4} .1449631{col 85}{space 3} .8602984
{txt}{space 23}Poland  {c |}{col 32}{res}{space 2} .1610275{col 44}{space 2} .1752509{col 55}{space 1}    0.92{col 64}{space 3}0.358{col 72}{space 4}-.1824578{col 85}{space 3} .5045129
{txt}{space 24}Spain  {c |}{col 32}{res}{space 2}-1.708367{col 44}{space 2} .2478413{col 55}{space 1}   -6.89{col 64}{space 3}0.000{col 72}{space 4}-2.194127{col 85}{space 3}-1.222607
{txt}{space 23}Sweden  {c |}{col 32}{res}{space 2}-1.836468{col 44}{space 2} .4090653{col 55}{space 1}   -4.49{col 64}{space 3}0.000{col 72}{space 4}-2.638222{col 85}{space 3}-1.034715
{txt}{space 30} {c |}
country#c.w5_newsexposure_mean {c |}
{space 22}Denmark  {c |}{col 32}{res}{space 2}-.0766552{col 44}{space 2} .0469947{col 55}{space 1}   -1.63{col 64}{space 3}0.103{col 72}{space 4}-.1687632{col 85}{space 3} .0154527
{txt}{space 23}France  {c |}{col 32}{res}{space 2} .3157381{col 44}{space 2} .0712005{col 55}{space 1}    4.43{col 64}{space 3}0.000{col 72}{space 4} .1761877{col 85}{space 3} .4552885
{txt}{space 22}Germany  {c |}{col 32}{res}{space 2} .0376678{col 44}{space 2}  .057502{col 55}{space 1}    0.66{col 64}{space 3}0.512{col 72}{space 4}-.0750339{col 85}{space 3} .1503696
{txt}{space 23}Greece  {c |}{col 32}{res}{space 2}  .094705{col 44}{space 2} .0471448{col 55}{space 1}    2.01{col 64}{space 3}0.045{col 72}{space 4} .0023029{col 85}{space 3}  .187107
{txt}{space 22}Hungary  {c |}{col 32}{res}{space 2} .0349626{col 44}{space 2} .0441815{col 55}{space 1}    0.79{col 64}{space 3}0.429{col 72}{space 4}-.0516315{col 85}{space 3} .1215567
{txt}{space 18}Netherlands  {c |}{col 32}{res}{space 2}-.0543684{col 44}{space 2} .0462233{col 55}{space 1}   -1.18{col 64}{space 3}0.240{col 72}{space 4}-.1449643{col 85}{space 3} .0362276
{txt}{space 23}Poland  {c |}{col 32}{res}{space 2}-.0167769{col 44}{space 2} .0430314{col 55}{space 1}   -0.39{col 64}{space 3}0.697{col 72}{space 4}-.1011169{col 85}{space 3} .0675631
{txt}{space 24}Spain  {c |}{col 32}{res}{space 2} .2319125{col 44}{space 2} .0580764{col 55}{space 1}    3.99{col 64}{space 3}0.000{col 72}{space 4} .1180848{col 85}{space 3} .3457402
{txt}{space 23}Sweden  {c |}{col 32}{res}{space 2}  .219622{col 44}{space 2} .1007924{col 55}{space 1}    2.18{col 64}{space 3}0.029{col 72}{space 4} .0220727{col 85}{space 3} .4171714
{txt}{space 30} {c |}
{space 14}knowledge_stable {c |}{col 32}{res}{space 2} .1099881{col 44}{space 2} .0081423{col 55}{space 1}   13.51{col 64}{space 3}0.000{col 72}{space 4} .0940296{col 85}{space 3} .1259466
{txt}{space 19}intefficacy {c |}{col 32}{res}{space 2} .0755122{col 44}{space 2} .0081836{col 55}{space 1}    9.23{col 64}{space 3}0.000{col 72}{space 4} .0594727{col 85}{space 3} .0915518
{txt}{space 13}w5_polinterest_eu {c |}{col 32}{res}{space 2} .1892453{col 44}{space 2} .0073025{col 55}{space 1}   25.92{col 64}{space 3}0.000{col 72}{space 4} .1749326{col 85}{space 3}  .203558
{txt}{space 11}turnoutintention_w5 {c |}{col 32}{res}{space 2} .0096464{col 44}{space 2} .0055688{col 55}{space 1}    1.73{col 64}{space 3}0.083{col 72}{space 4}-.0012683{col 85}{space 3}  .020561
{txt}{space 17}euintegration {c |}{col 32}{res}{space 2} .0297705{col 44}{space 2} .0053067{col 55}{space 1}    5.61{col 64}{space 3}0.000{col 72}{space 4} .0193696{col 85}{space 3} .0401715
{txt}{space 24}female {c |}{col 32}{res}{space 2}-.0647624{col 44}{space 2} .0178297{col 55}{space 1}   -3.63{col 64}{space 3}0.000{col 72}{space 4}-.0997079{col 85}{space 3}-.0298169
{txt}{space 27}age {c |}{col 32}{res}{space 2}-.0003725{col 44}{space 2} .0006042{col 55}{space 1}   -0.62{col 64}{space 3}0.537{col 72}{space 4}-.0015567{col 85}{space 3} .0008116
{txt}{space 21}education {c |}{col 32}{res}{space 2} .0213482{col 44}{space 2} .0051219{col 55}{space 1}    4.17{col 64}{space 3}0.000{col 72}{space 4} .0113094{col 85}{space 3} .0313869
{txt}{space 17}OwnCandidate2 {c |}{col 32}{res}{space 2}        0{col 44}{txt}  (omitted)
{space 25}_cons {c |}{col 32}{res}{space 2}-1.657372{col 44}{space 2} .1527781{col 55}{space 1}  -10.85{col 64}{space 3}0.000{col 72}{space 4}-1.956812{col 85}{space 3}-1.357933
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}/lnalpha {c |}{col 32}{res}{space 2}-1.255868{col 44}{space 2} .0538602{col 72}{space 4}-1.361432{col 85}{space 3}-1.150304
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         alpha {c |}{col 32}{res}{space 2} .2848284{col 44}{space 2} .0153409{col 72}{space 4} .2562934{col 85}{space 3} .3165405
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. * leads to multicollinearity between OwnCandidate2 and country - omit OwnCandidate2
. 
. *** Table A12 - model 1
. 
. nbreg n_cand_rec w5_newsexposure_mean knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration female age education i.country, cluster(ID) nolog
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}   119,189
{txt}{col 49}Wald chi2({res}18{txt}){col 67}= {res}   5066.02
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-167568.33{txt}{col 49}Pseudo R2{col 67}= {res}    0.0976

{txt}{ralign 86:(Std. Err. adjusted for {res:17,027} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}          n_cand_rec{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
w5_newsexposure_mean {c |}{col 22}{res}{space 2} .1402867{col 34}{space 2}  .011775{col 45}{space 1}   11.91{col 54}{space 3}0.000{col 62}{space 4} .1172081{col 75}{space 3} .1633654
{txt}{space 4}knowledge_stable {c |}{col 22}{res}{space 2} .1078541{col 34}{space 2} .0081747{col 45}{space 1}   13.19{col 54}{space 3}0.000{col 62}{space 4} .0918319{col 75}{space 3} .1238763
{txt}{space 9}intefficacy {c |}{col 22}{res}{space 2}   .07596{col 34}{space 2}  .008231{col 45}{space 1}    9.23{col 54}{space 3}0.000{col 62}{space 4} .0598276{col 75}{space 3} .0920924
{txt}{space 3}w5_polinterest_eu {c |}{col 22}{res}{space 2} .1901404{col 34}{space 2} .0073038{col 45}{space 1}   26.03{col 54}{space 3}0.000{col 62}{space 4} .1758253{col 75}{space 3} .2044555
{txt}{space 1}turnoutintention_w5 {c |}{col 22}{res}{space 2} .0100461{col 34}{space 2} .0055729{col 45}{space 1}    1.80{col 54}{space 3}0.071{col 62}{space 4}-.0008765{col 75}{space 3} .0209687
{txt}{space 7}euintegration {c |}{col 22}{res}{space 2} .0311542{col 34}{space 2} .0053213{col 45}{space 1}    5.85{col 54}{space 3}0.000{col 62}{space 4} .0207247{col 75}{space 3} .0415837
{txt}{space 14}female {c |}{col 22}{res}{space 2}-.0658715{col 34}{space 2} .0179701{col 45}{space 1}   -3.67{col 54}{space 3}0.000{col 62}{space 4}-.1010923{col 75}{space 3}-.0306507
{txt}{space 17}age {c |}{col 22}{res}{space 2}-.0006475{col 34}{space 2} .0006051{col 45}{space 1}   -1.07{col 54}{space 3}0.285{col 62}{space 4}-.0018334{col 75}{space 3} .0005384
{txt}{space 11}education {c |}{col 22}{res}{space 2} .0204306{col 34}{space 2} .0051603{col 45}{space 1}    3.96{col 54}{space 3}0.000{col 62}{space 4} .0103166{col 75}{space 3} .0305447
{txt}{space 20} {c |}
{space 13}country {c |}
{space 12}Denmark  {c |}{col 22}{res}{space 2} .0659369{col 34}{space 2}  .035073{col 45}{space 1}    1.88{col 54}{space 3}0.060{col 62}{space 4} -.002805{col 75}{space 3} .1346788
{txt}{space 13}France  {c |}{col 22}{res}{space 2}-.9533188{col 34}{space 2} .0550722{col 45}{space 1}  -17.31{col 54}{space 3}0.000{col 62}{space 4}-1.061258{col 75}{space 3}-.8453792
{txt}{space 12}Germany  {c |}{col 22}{res}{space 2}-.4640752{col 34}{space 2} .0436517{col 45}{space 1}  -10.63{col 54}{space 3}0.000{col 62}{space 4}-.5496309{col 75}{space 3}-.3785194
{txt}{space 13}Greece  {c |}{col 22}{res}{space 2}-.4290143{col 34}{space 2} .0416832{col 45}{space 1}  -10.29{col 54}{space 3}0.000{col 62}{space 4} -.510712{col 75}{space 3}-.3473167
{txt}{space 12}Hungary  {c |}{col 22}{res}{space 2} .0814784{col 34}{space 2} .0382161{col 45}{space 1}    2.13{col 54}{space 3}0.033{col 62}{space 4} .0065762{col 75}{space 3} .1563806
{txt}{space 8}Netherlands  {c |}{col 22}{res}{space 2} .2921097{col 34}{space 2} .0345903{col 45}{space 1}    8.44{col 54}{space 3}0.000{col 62}{space 4} .2243139{col 75}{space 3} .3599054
{txt}{space 13}Poland  {c |}{col 22}{res}{space 2} .0652504{col 34}{space 2} .0363829{col 45}{space 1}    1.79{col 54}{space 3}0.073{col 62}{space 4}-.0060588{col 75}{space 3} .1365596
{txt}{space 14}Spain  {c |}{col 22}{res}{space 2}-.7459963{col 34}{space 2}   .04988{col 45}{space 1}  -14.96{col 54}{space 3}0.000{col 62}{space 4}-.8437593{col 75}{space 3}-.6482334
{txt}{space 13}Sweden  {c |}{col 22}{res}{space 2}-.9737632{col 34}{space 2} .0575575{col 45}{space 1}  -16.92{col 54}{space 3}0.000{col 62}{space 4}-1.086574{col 75}{space 3}-.8609525
{txt}{space 20} {c |}
{space 15}_cons {c |}{col 22}{res}{space 2}-1.868656{col 34}{space 2}  .071846{col 45}{space 1}  -26.01{col 54}{space 3}0.000{col 62}{space 4}-2.009471{col 75}{space 3} -1.72784
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2}-1.218223{col 34}{space 2} .0519288{col 62}{space 4}-1.320002{col 75}{space 3}-1.116445
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} .2957552{col 34}{space 2} .0153582{col 62}{space 4} .2671349{col 75}{space 3} .3274419
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *** Table A12 - model 2
. 
. nbreg n_cand_rec c.w5_newsexposure_mean##country knowledge_stable intefficacy w5_polinterest_eu turnoutintention_w5 euintegration female age education, cluster(ID) nolog
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}   119,189
{txt}{col 49}Wald chi2({res}27{txt}){col 67}= {res}   4922.49
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-167104.12{txt}{col 49}Pseudo R2{col 67}= {res}    0.1001

{txt}{ralign 96:(Std. Err. adjusted for {res:17,027} clusters in ID)}
{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                    n_cand_rec{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}w5_newsexposure_mean {c |}{col 32}{res}{space 2} .0816079{col 44}{space 2} .0372737{col 55}{space 1}    2.19{col 64}{space 3}0.029{col 72}{space 4} .0085528{col 85}{space 3}  .154663
{txt}{space 30} {c |}
{space 23}country {c |}
{space 22}Denmark  {c |}{col 32}{res}{space 2} .3629915{col 44}{space 2} .1834923{col 55}{space 1}    1.98{col 64}{space 3}0.048{col 72}{space 4} .0033532{col 85}{space 3} .7226298
{txt}{space 23}France  {c |}{col 32}{res}{space 2}-2.227001{col 44}{space 2} .3028421{col 55}{space 1}   -7.35{col 64}{space 3}0.000{col 72}{space 4}-2.820561{col 85}{space 3}-1.633442
{txt}{space 22}Germany  {c |}{col 32}{res}{space 2}-.6005971{col 44}{space 2} .2321907{col 55}{space 1}   -2.59{col 64}{space 3}0.010{col 72}{space 4}-1.055683{col 85}{space 3}-.1455116
{txt}{space 23}Greece  {c |}{col 32}{res}{space 2}-.7866012{col 44}{space 2} .1888233{col 55}{space 1}   -4.17{col 64}{space 3}0.000{col 72}{space 4}-1.156688{col 85}{space 3}-.4165144
{txt}{space 22}Hungary  {c |}{col 32}{res}{space 2}-.0539455{col 44}{space 2} .1743893{col 55}{space 1}   -0.31{col 64}{space 3}0.757{col 72}{space 4}-.3957422{col 85}{space 3} .2878511
{txt}{space 18}Netherlands  {c |}{col 32}{res}{space 2} .5026308{col 44}{space 2} .1824868{col 55}{space 1}    2.75{col 64}{space 3}0.006{col 72}{space 4} .1449631{col 85}{space 3} .8602984
{txt}{space 23}Poland  {c |}{col 32}{res}{space 2} .1610275{col 44}{space 2} .1752509{col 55}{space 1}    0.92{col 64}{space 3}0.358{col 72}{space 4}-.1824578{col 85}{space 3} .5045129
{txt}{space 24}Spain  {c |}{col 32}{res}{space 2}-1.708367{col 44}{space 2} .2478413{col 55}{space 1}   -6.89{col 64}{space 3}0.000{col 72}{space 4}-2.194127{col 85}{space 3}-1.222607
{txt}{space 23}Sweden  {c |}{col 32}{res}{space 2}-1.836468{col 44}{space 2} .4090653{col 55}{space 1}   -4.49{col 64}{space 3}0.000{col 72}{space 4}-2.638222{col 85}{space 3}-1.034715
{txt}{space 30} {c |}
country#c.w5_newsexposure_mean {c |}
{space 22}Denmark  {c |}{col 32}{res}{space 2}-.0766552{col 44}{space 2} .0469947{col 55}{space 1}   -1.63{col 64}{space 3}0.103{col 72}{space 4}-.1687632{col 85}{space 3} .0154527
{txt}{space 23}France  {c |}{col 32}{res}{space 2} .3157381{col 44}{space 2} .0712005{col 55}{space 1}    4.43{col 64}{space 3}0.000{col 72}{space 4} .1761877{col 85}{space 3} .4552885
{txt}{space 22}Germany  {c |}{col 32}{res}{space 2} .0376678{col 44}{space 2}  .057502{col 55}{space 1}    0.66{col 64}{space 3}0.512{col 72}{space 4}-.0750339{col 85}{space 3} .1503696
{txt}{space 23}Greece  {c |}{col 32}{res}{space 2}  .094705{col 44}{space 2} .0471448{col 55}{space 1}    2.01{col 64}{space 3}0.045{col 72}{space 4} .0023029{col 85}{space 3}  .187107
{txt}{space 22}Hungary  {c |}{col 32}{res}{space 2} .0349626{col 44}{space 2} .0441815{col 55}{space 1}    0.79{col 64}{space 3}0.429{col 72}{space 4}-.0516315{col 85}{space 3} .1215567
{txt}{space 18}Netherlands  {c |}{col 32}{res}{space 2}-.0543684{col 44}{space 2} .0462233{col 55}{space 1}   -1.18{col 64}{space 3}0.240{col 72}{space 4}-.1449643{col 85}{space 3} .0362276
{txt}{space 23}Poland  {c |}{col 32}{res}{space 2}-.0167769{col 44}{space 2} .0430314{col 55}{space 1}   -0.39{col 64}{space 3}0.697{col 72}{space 4}-.1011169{col 85}{space 3} .0675631
{txt}{space 24}Spain  {c |}{col 32}{res}{space 2} .2319125{col 44}{space 2} .0580764{col 55}{space 1}    3.99{col 64}{space 3}0.000{col 72}{space 4} .1180848{col 85}{space 3} .3457402
{txt}{space 23}Sweden  {c |}{col 32}{res}{space 2}  .219622{col 44}{space 2} .1007924{col 55}{space 1}    2.18{col 64}{space 3}0.029{col 72}{space 4} .0220727{col 85}{space 3} .4171714
{txt}{space 30} {c |}
{space 14}knowledge_stable {c |}{col 32}{res}{space 2} .1099881{col 44}{space 2} .0081423{col 55}{space 1}   13.51{col 64}{space 3}0.000{col 72}{space 4} .0940296{col 85}{space 3} .1259466
{txt}{space 19}intefficacy {c |}{col 32}{res}{space 2} .0755122{col 44}{space 2} .0081836{col 55}{space 1}    9.23{col 64}{space 3}0.000{col 72}{space 4} .0594727{col 85}{space 3} .0915518
{txt}{space 13}w5_polinterest_eu {c |}{col 32}{res}{space 2} .1892453{col 44}{space 2} .0073025{col 55}{space 1}   25.92{col 64}{space 3}0.000{col 72}{space 4} .1749326{col 85}{space 3}  .203558
{txt}{space 11}turnoutintention_w5 {c |}{col 32}{res}{space 2} .0096464{col 44}{space 2} .0055688{col 55}{space 1}    1.73{col 64}{space 3}0.083{col 72}{space 4}-.0012683{col 85}{space 3}  .020561
{txt}{space 17}euintegration {c |}{col 32}{res}{space 2} .0297705{col 44}{space 2} .0053067{col 55}{space 1}    5.61{col 64}{space 3}0.000{col 72}{space 4} .0193696{col 85}{space 3} .0401715
{txt}{space 24}female {c |}{col 32}{res}{space 2}-.0647624{col 44}{space 2} .0178297{col 55}{space 1}   -3.63{col 64}{space 3}0.000{col 72}{space 4}-.0997079{col 85}{space 3}-.0298169
{txt}{space 27}age {c |}{col 32}{res}{space 2}-.0003725{col 44}{space 2} .0006042{col 55}{space 1}   -0.62{col 64}{space 3}0.537{col 72}{space 4}-.0015567{col 85}{space 3} .0008116
{txt}{space 21}education {c |}{col 32}{res}{space 2} .0213482{col 44}{space 2} .0051219{col 55}{space 1}    4.17{col 64}{space 3}0.000{col 72}{space 4} .0113094{col 85}{space 3} .0313869
{txt}{space 25}_cons {c |}{col 32}{res}{space 2}-1.657372{col 44}{space 2} .1527781{col 55}{space 1}  -10.85{col 64}{space 3}0.000{col 72}{space 4}-1.956812{col 85}{space 3}-1.357933
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}/lnalpha {c |}{col 32}{res}{space 2}-1.255868{col 44}{space 2} .0538602{col 72}{space 4}-1.361432{col 85}{space 3}-1.150304
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         alpha {c |}{col 32}{res}{space 2} .2848284{col 44}{space 2} .0153409{col 72}{space 4} .2562934{col 85}{space 3} .3165405
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *** Figure A2
. 
. margins, at(w5_newsexposure_mean=(1(1)7) country=(1(1)10))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}   119,189
{txt}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted number of events, predict()}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:3._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:4._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:5._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:6._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:7._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}7}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:8._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}8}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:9._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}9}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:10._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 9}10}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:11._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}1}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:12._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:13._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}3}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:14._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}4}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:15._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}5}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:16._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}6}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:17._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}7}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:18._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}8}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:19._at}:{space 1}{res:{txt:w5_newsexp~n}{space 4}{txt:=} {space 10}2}{p_end}
{p2colreset}{...}
{txt}{p2colset 1 14 16 2}{...}
{p2col: }{space 2}{res:{txt:country}{space 9}{txt:=} {space 10}9}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
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{res}{txt}
{com}. marginsplot, recast(line) noci yline(0)

{text}{p 2 6 2}Variables that uniquely identify margins: w5_newsexposure_mean country{p_end}
{res}{txt}
{com}. 
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{txt}end of do-file

{com}. log close
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